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International Conference on Computational Linguistics 1

https://aclanthology.org/2024.lrec-main.101.bib

https://aclanthology.org/2024.lrec-main.101/

@inproceedings{alva-principe-etal-2024-lcf, title = "An {LCF}-{IDF} Document Representation Model Applied to Long Document Classification", author = "Alva Principe, Renzo Arturo and Chiarini, Nicola and Viviani, Marco", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.101", pages = "1129--1135", abstract = "A document representation model that has been used for years in NLP and Text Mining tasks is TF-IDF (Term Frequency-Inverse Document Frequency). This model is indeed effective for various tasks like Information Retrieval and Document Classification. However, it may fall short when it comes to capturing the deeper semantic and contextual meaning of a text, which is where Transformer-based Pre-trained Language Models (PLMs) such as BERT have been gaining significant traction in recent years. Despite this, these models also face specific challenges related to Transformers and their attention mechanism limits, especially when dealing with long documents. Therefore, this paper proposes a novel approach to exploit the advantages of the TF-IDF representation while incorporating semantic context, by introducing a Latent Concept Frequency-Inverse Document Frequency (LCF-IDF) document representation model. Its effectiveness is tested with respect to the Long Document Classification task. The results obtained show promising performance of the proposed solution compared to TF-IDF and BERT-like representation models, including those specifically for long documents such as Longformer as well as those designed for particular domains, especially when it comes to Single Label Multi-Class (SLMC) classification.", }

A document representation model that has been used for years in NLP and Text Mining tasks is TF-IDF (Term Frequency-Inverse Document Frequency). This model is indeed effective for various tasks like Information Retrieval and Document Classification. However, it may fall short when it comes to capturing the deeper semantic and contextual meaning of a text, which is where Transformer-based Pre-trained Language Models (PLMs) such as BERT have been gaining significant traction in recent years. Despite this, these models also face specific challenges related to Transformers and their attention mechanism limits, especially when dealing with long documents. Therefore, this paper proposes a novel approach to exploit the advantages of the TF-IDF representation while incorporating semantic context, by introducing a Latent Concept Frequency-Inverse Document Frequency (LCF-IDF) document representation model. Its effectiveness is tested with respect to the Long Document Classification task. The results obtained show promising performance of the proposed solution compared to TF-IDF and BERT-like representation models, including those specifically for long documents such as Longformer as well as those designed for particular domains, especially when it comes to Single Label Multi-Class (SLMC) classification.

An LCF-IDF Document Representation Model Applied to Long Document Classification

lrec-main.101

Poster

https://aclanthology.org/2024.lrec-main.102.bib

https://aclanthology.org/2024.lrec-main.102/

@inproceedings{tan-etal-2024-llm, title = "An {LLM}-Enhanced Adversarial Editing System for Lexical Simplification", author = "Tan, Keren and Luo, Kangyang and Lan, Yunshi and Yuan, Zheng and Shu, Jinlong", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.102", pages = "1136--1146", abstract = "Lexical Simplification (LS) aims to simplify text at the lexical level. Existing methods rely heavily on annotated data, making it challenging to apply in low-resource scenarios. In this paper, we propose a novel LS method without parallel corpora. This method employs an Adversarial Editing System with guidance from a confusion loss and an invariance loss to predict lexical edits in the original sentences. Meanwhile, we introduce an innovative LLM-enhanced loss to enable the distillation of knowledge from Large Language Models (LLMs) into a small-size LS system. From that, complex words within sentences are masked and a Difficulty-aware Filling module is crafted to replace masked positions with simpler words. At last, extensive experimental results and analyses on three benchmark LS datasets demonstrate the effectiveness of our proposed method.", }

Lexical Simplification (LS) aims to simplify text at the lexical level. Existing methods rely heavily on annotated data, making it challenging to apply in low-resource scenarios. In this paper, we propose a novel LS method without parallel corpora. This method employs an Adversarial Editing System with guidance from a confusion loss and an invariance loss to predict lexical edits in the original sentences. Meanwhile, we introduce an innovative LLM-enhanced loss to enable the distillation of knowledge from Large Language Models (LLMs) into a small-size LS system. From that, complex words within sentences are masked and a Difficulty-aware Filling module is crafted to replace masked positions with simpler words. At last, extensive experimental results and analyses on three benchmark LS datasets demonstrate the effectiveness of our proposed method.

An LLM-Enhanced Adversarial Editing System for Lexical Simplification

lrec-main.102

Poster

2402.14704

https://aclanthology.org/2024.lrec-main.103.bib

https://aclanthology.org/2024.lrec-main.103/

@inproceedings{lange-etal-2024-annoctr, title = "{A}nno{CTR}: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports", author = {Lange, Lukas and M{\"u}ller, Marc and Haratinezhad Torbati, Ghazaleh and Milchevski, Dragan and Grau, Patrick and Pujari, Subhash Chandra and Friedrich, Annemarie}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.103", pages = "1147--1160", abstract = "Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically distributed using natural language reports. Natural language processing can help with managing this large amount of unstructured information, yet to date, the topic has received little attention. With this paper, we present AnnoCTR, a new CC-BY-SA-licensed dataset of cyber threat reports. The reports have been annotated by a domain expert with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. Entities and concepts are linked to Wikipedia and the MITRE ATT{\&}CK knowledge base, the most widely-used taxonomy for classifying types of attacks. Prior datasets linking to MITRE ATT{\&}CK either provide a single label per document or annotate sentences out-of-context; our dataset annotates entire documents in a much finer-grained way. In an experimental study, we model the annotations of our dataset using state-of-the-art neural models. In our few-shot scenario, we find that for identifying the MITRE ATT{\&}CK concepts that are mentioned explicitly or implicitly in a text, concept descriptions from MITRE ATT{\&}CK are an effective source for training data augmentation.", }

Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically distributed using natural language reports. Natural language processing can help with managing this large amount of unstructured information, yet to date, the topic has received little attention. With this paper, we present AnnoCTR, a new CC-BY-SA-licensed dataset of cyber threat reports. The reports have been annotated by a domain expert with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. Entities and concepts are linked to Wikipedia and the MITRE ATT{\&}CK knowledge base, the most widely-used taxonomy for classifying types of attacks. Prior datasets linking to MITRE ATT{\&}CK either provide a single label per document or annotate sentences out-of-context; our dataset annotates entire documents in a much finer-grained way. In an experimental study, we model the annotations of our dataset using state-of-the-art neural models. In our few-shot scenario, we find that for identifying the MITRE ATT{\&}CK concepts that are mentioned explicitly or implicitly in a text, concept descriptions from MITRE ATT{\&}CK are an effective source for training data augmentation.

AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports

lrec-main.103

Poster

2404.07765

https://huggingface.co/papers/2404.07765

https://aclanthology.org/2024.lrec-main.104.bib

https://aclanthology.org/2024.lrec-main.104/

@inproceedings{ge-etal-2024-annotate, title = "Annotate {C}hinese Aspect with {UMR}{---}{---}a Case Study on the Liitle Prince", author = "Ge, Sijia and Li, Zilong and Chen, Alvin Po-Chun and Wang, Guanchao", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.104", pages = "1161--1172", abstract = "Aspect is a valuable tool for determining the perspective from which an event is observed, allowing for viewing both at the situation and viewpoint level. Uniform Meaning Representation (UMR) seeks to provide a standard, typologically-informed representation of aspects across languages. It employs an aspectual lattice to adapt to different languages and design values that encompass both viewpoint aspect and situation aspects. In the context of annotating the Chinese version of The Little Prince, we paid particular attention to the interactions between aspect values and aspect markers and we also want to know the annotation effectiveness and challenges under the UMR aspectual lattice. During our annotation process, we identified the relationships between aspectual markers and labels. We further categorized and analyzed complex examples that led to low inter-annotator agreement. The factors contributing to disagreement among annotators included the interpretations of lexical semantics, implications, and the influence of aspectual markers, which is related to the inclination of the situation aspect and the exclusivity between the two aspects{'} perspectives. Overall, our work sheds light on the challenges of aspect annotation in Chinese and highlights the need for more comprehensive guidelines.", }

Aspect is a valuable tool for determining the perspective from which an event is observed, allowing for viewing both at the situation and viewpoint level. Uniform Meaning Representation (UMR) seeks to provide a standard, typologically-informed representation of aspects across languages. It employs an aspectual lattice to adapt to different languages and design values that encompass both viewpoint aspect and situation aspects. In the context of annotating the Chinese version of The Little Prince, we paid particular attention to the interactions between aspect values and aspect markers and we also want to know the annotation effectiveness and challenges under the UMR aspectual lattice. During our annotation process, we identified the relationships between aspectual markers and labels. We further categorized and analyzed complex examples that led to low inter-annotator agreement. The factors contributing to disagreement among annotators included the interpretations of lexical semantics, implications, and the influence of aspectual markers, which is related to the inclination of the situation aspect and the exclusivity between the two aspects{'} perspectives. Overall, our work sheds light on the challenges of aspect annotation in Chinese and highlights the need for more comprehensive guidelines.

Annotate Chinese Aspect with UMR——a Case Study on the Liitle Prince

lrec-main.104

Poster

https://aclanthology.org/2024.lrec-main.105.bib

https://aclanthology.org/2024.lrec-main.105/

@inproceedings{zhang-etal-2024-annotate, title = "Annotate the Way You Think: An Incremental Note Generation Framework for the Summarization of Medical Conversations", author = "Zhang, Longxiang and Hart, Caleb D. and Burger, Susanne and Schaaf, Thomas", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.105", pages = "1173--1186", abstract = "The scarcity of public datasets for the summarization of medical conversations has been a limiting factor for advancing NLP research in the healthcare domain, and the structure of the existing data is largely limited to the simple format of conversation-summary pairs. We therefore propose a novel Incremental Note Generation (ING) annotation framework capable of greatly enriching summarization datasets in the healthcare domain and beyond. Our framework is designed to capture the human summarization process via an annotation task by instructing the annotators to first incrementally create a draft note as they accumulate information through a conversation transcript (Generation) and then polish the draft note into a reference note (Rewriting). The annotation results include both the reference note and a comprehensive editing history of the draft note in tabular format. Our pilot study on the task of SOAP note generation showed reasonable consistency between four expert annotators, established a solid baseline for quantitative targets of inter-rater agreement, and demonstrated the ING framework as an improvement over the traditional annotation process for future modeling of summarization.", }

The scarcity of public datasets for the summarization of medical conversations has been a limiting factor for advancing NLP research in the healthcare domain, and the structure of the existing data is largely limited to the simple format of conversation-summary pairs. We therefore propose a novel Incremental Note Generation (ING) annotation framework capable of greatly enriching summarization datasets in the healthcare domain and beyond. Our framework is designed to capture the human summarization process via an annotation task by instructing the annotators to first incrementally create a draft note as they accumulate information through a conversation transcript (Generation) and then polish the draft note into a reference note (Rewriting). The annotation results include both the reference note and a comprehensive editing history of the draft note in tabular format. Our pilot study on the task of SOAP note generation showed reasonable consistency between four expert annotators, established a solid baseline for quantitative targets of inter-rater agreement, and demonstrated the ING framework as an improvement over the traditional annotation process for future modeling of summarization.

Annotate the Way You Think: An Incremental Note Generation Framework for the Summarization of Medical Conversations

lrec-main.105

Poster

https://aclanthology.org/2024.lrec-main.106.bib

https://aclanthology.org/2024.lrec-main.106/

@inproceedings{xu-etal-2024-annotating, title = "Annotating {C}hinese Word Senses with {E}nglish {W}ord{N}et: A Practice on {O}nto{N}otes {C}hinese Sense Inventories", author = "Xu, Hongzhi and Lin, Jingxia and Pradhan, Sameer and Marcus, Mitchell and Liu, Ming", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.106", pages = "1187--1196", abstract = "In this paper, we present our exploration of annotating Chinese word senses using English WordNet synsets, with examples extracted from OntoNotes Chinese sense inventories. Given a target word along with the example that contains it, the annotators select a WordNet synset that best describes the meaning of the target word in the context. The result demonstrates an inter-annotator agreement of 38{\%} between two annotators. We delve into the instances of disagreement by comparing the two annotated synsets, including their positions within the WordNet hierarchy. The examination reveals intriguing patterns among closely related synsets, shedding light on similar concepts represented within the WordNet structure. The data offers as an indirect linking of Chinese word senses defined in OntoNotes Chinese sense inventories to WordNet sysnets, and thus promotes the value of the OntoNotes corpus. Compared to a direct linking of Chinese word senses to WordNet synsets, the example-based annotation has the merit of not being affected by inaccurate sense definitions and thus offers a new way of mapping WordNets of different languages. At the same time, the annotated data also serves as a valuable linguistic resource for exploring potential lexical differences between English and Chinese, with potential contributions to the broader understanding of cross-linguistic semantic mapping", }

In this paper, we present our exploration of annotating Chinese word senses using English WordNet synsets, with examples extracted from OntoNotes Chinese sense inventories. Given a target word along with the example that contains it, the annotators select a WordNet synset that best describes the meaning of the target word in the context. The result demonstrates an inter-annotator agreement of 38{\%} between two annotators. We delve into the instances of disagreement by comparing the two annotated synsets, including their positions within the WordNet hierarchy. The examination reveals intriguing patterns among closely related synsets, shedding light on similar concepts represented within the WordNet structure. The data offers as an indirect linking of Chinese word senses defined in OntoNotes Chinese sense inventories to WordNet sysnets, and thus promotes the value of the OntoNotes corpus. Compared to a direct linking of Chinese word senses to WordNet synsets, the example-based annotation has the merit of not being affected by inaccurate sense definitions and thus offers a new way of mapping WordNets of different languages. At the same time, the annotated data also serves as a valuable linguistic resource for exploring potential lexical differences between English and Chinese, with potential contributions to the broader understanding of cross-linguistic semantic mapping

Annotating Chinese Word Senses with English WordNet: A Practice on OntoNotes Chinese Sense Inventories

lrec-main.106

Poster

https://aclanthology.org/2024.lrec-main.107.bib

https://aclanthology.org/2024.lrec-main.107/

@inproceedings{stock-etal-2024-annotating, title = "Annotating Customer-Oriented Behaviour in Call Centre Sales Dialogues", author = "Stock, Jutta and Petukhova, Volha and Klakow, Dietrich", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.107", pages = "1197--1208", abstract = "Customer-oriented behaviour (COB) plays an important role in call centre interactions, particularly in the context of successful sales negotiation. However, the evaluation of COB in customer-agent conversations often lacks clarity in its definition and robust computational assessment methods. This paper addresses these challenges by presenting a comprehensive conceptual and empirical framework. We conducted multidimensional dialogue act annotations on authentic call centre interactions using the ISO 24617-2 taxonomy, capturing the multifaceted nature of these interactions. This process led to the identification of relevant dialogue act categories, proposed extensions concerning relationship-building aspects, and derived corpus statistics. The findings highlight specific facets of COB that positively impact on Customer Satisfaction (CS), as determined through correlation analysis. Additionally, we delved into the dependencies between COB and feedback acts, leveraging the hierarchical structure of the DIT++ model. This framework improves our understanding of the dynamics shaping sales strategies in call centres and holds promise for practical applications in optimising customer-agent interactions.", }

Customer-oriented behaviour (COB) plays an important role in call centre interactions, particularly in the context of successful sales negotiation. However, the evaluation of COB in customer-agent conversations often lacks clarity in its definition and robust computational assessment methods. This paper addresses these challenges by presenting a comprehensive conceptual and empirical framework. We conducted multidimensional dialogue act annotations on authentic call centre interactions using the ISO 24617-2 taxonomy, capturing the multifaceted nature of these interactions. This process led to the identification of relevant dialogue act categories, proposed extensions concerning relationship-building aspects, and derived corpus statistics. The findings highlight specific facets of COB that positively impact on Customer Satisfaction (CS), as determined through correlation analysis. Additionally, we delved into the dependencies between COB and feedback acts, leveraging the hierarchical structure of the DIT++ model. This framework improves our understanding of the dynamics shaping sales strategies in call centres and holds promise for practical applications in optimising customer-agent interactions.

Annotating Customer-Oriented Behaviour in Call Centre Sales Dialogues

lrec-main.107

Poster

https://aclanthology.org/2024.lrec-main.108.bib

https://aclanthology.org/2024.lrec-main.108/

@inproceedings{bizzaro-etal-2024-annotation, title = "Annotation and Classification of Relevant Clauses in Terms-and-Conditions Contracts", author = "Bizzaro, Pietro Giovanni and Della Valentina, Elena and Napolitano, Maurizio and Mana, Nadia and Zancanaro, Massimo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.108", pages = "1209--1214", abstract = "In this paper, we propose a new annotation scheme to classify different types of clauses in Terms-and-Conditions contracts with the ultimate goal of supporting legal experts to quickly identify and assess problematic issues in this type of legal documents. To this end, we built a small corpus of Terms-and-Conditions contracts and finalized an annotation scheme of 14 categories, eventually reaching an inter-annotator agreement of 0.92. Then, for 11 of them, we experimented with binary classification tasks using few-shot prompting with a multilingual T5 and two fine-tuned versions of two BERT-based LLMs for Italian. Our experiments showed the feasibility of automatic classification of our categories by reaching accuracies ranging from .79 to .95 on validation tasks.", }

In this paper, we propose a new annotation scheme to classify different types of clauses in Terms-and-Conditions contracts with the ultimate goal of supporting legal experts to quickly identify and assess problematic issues in this type of legal documents. To this end, we built a small corpus of Terms-and-Conditions contracts and finalized an annotation scheme of 14 categories, eventually reaching an inter-annotator agreement of 0.92. Then, for 11 of them, we experimented with binary classification tasks using few-shot prompting with a multilingual T5 and two fine-tuned versions of two BERT-based LLMs for Italian. Our experiments showed the feasibility of automatic classification of our categories by reaching accuracies ranging from .79 to .95 on validation tasks.

Annotation and Classification of Relevant Clauses in Terms-and-Conditions Contracts

lrec-main.108

Poster

2402.14457

https://aclanthology.org/2024.lrec-main.109.bib

https://aclanthology.org/2024.lrec-main.109/

@inproceedings{kubota-etal-2024-annotation, title = "Annotation of {J}apanese Discourse Relations Focusing on Concessive Inferences", author = "Kubota, Ai and Sato, Takuma and Amamoto, Takayuki and Akiyoshi, Ryota and Mineshima, Koji", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.109", pages = "1215--1224", abstract = "In this study, we focus on the inference presupposed in the concessive discourse relation and present the discourse relation annotation for the Japanese connectives {`}nagara{'} and {`}tsutsu{'}, both of which have two usages: Synchronous and Concession, just like English while. We also present the annotation for {`}tokorode{'}, which is ambiguous in three ways: Temporal, Location, and Concession. While corpora containing concessive discourse relations already exist, the distinctive feature of our study is that it aims to identify the concessive inferential relations by writing out the implicit presupposed inferences. In this paper, we report on the annotation methodology and its results, as well as the characteristics of concession that became apparent during annotation.", }

In this study, we focus on the inference presupposed in the concessive discourse relation and present the discourse relation annotation for the Japanese connectives {`}nagara{'} and {`}tsutsu{'}, both of which have two usages: Synchronous and Concession, just like English while. We also present the annotation for {`}tokorode{'}, which is ambiguous in three ways: Temporal, Location, and Concession. While corpora containing concessive discourse relations already exist, the distinctive feature of our study is that it aims to identify the concessive inferential relations by writing out the implicit presupposed inferences. In this paper, we report on the annotation methodology and its results, as well as the characteristics of concession that became apparent during annotation.

Annotation of Japanese Discourse Relations Focusing on Concessive Inferences

lrec-main.109

Poster

https://aclanthology.org/2024.lrec-main.110.bib

https://aclanthology.org/2024.lrec-main.110/

@inproceedings{uro-etal-2024-annotation, title = "Annotation of Transition-Relevance Places and Interruptions for the Description of Turn-Taking in Conversations in {F}rench Media Content", author = "Uro, R{\'e}mi and Tahon, Marie and Wottawa, Jane and Doukhan, David and Rilliard, Albert and Laurent, Antoine", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.110", pages = "1225--1232", abstract = "Few speech resources describe interruption phenomena, especially for TV and media content. The description of these phenomena may vary across authors: it thus leaves room for improved annotation protocols. We present an annotation of Transition-Relevance Places (TRP) and Floor-Taking event types on an existing French TV and Radio broadcast corpus to facilitate studies of interruptions and turn-taking. Each speaker change is annotated with the presence or absence of a TRP, and a classification of the next-speaker floor-taking as Smooth, Backchannel or different types of turn violations (cooperative or competitive, successful or attempted interruption). An inter-rater agreement analysis shows such annotations{'} moderate to substantial reliability. The inter-annotator agreement for TRP annotation reaches κ=0.75, κ=0.56 for Backchannel and κ=0.5 for the Interruption/non-interruption distinction. More precise differences linked to cooperative or competitive behaviors lead to lower agreements. These results underline the importance of low-level features like TRP to derive a classification of turn changes that would be less subject to interpretation. The analysis of the presence of overlapping speech highlights the existence of interruptions without overlaps and smooth transitions with overlaps. These annotations are available at https://lium.univ-lemans.fr/corpus-allies/.", }

Few speech resources describe interruption phenomena, especially for TV and media content. The description of these phenomena may vary across authors: it thus leaves room for improved annotation protocols. We present an annotation of Transition-Relevance Places (TRP) and Floor-Taking event types on an existing French TV and Radio broadcast corpus to facilitate studies of interruptions and turn-taking. Each speaker change is annotated with the presence or absence of a TRP, and a classification of the next-speaker floor-taking as Smooth, Backchannel or different types of turn violations (cooperative or competitive, successful or attempted interruption). An inter-rater agreement analysis shows such annotations{'} moderate to substantial reliability. The inter-annotator agreement for TRP annotation reaches κ=0.75, κ=0.56 for Backchannel and κ=0.5 for the Interruption/non-interruption distinction. More precise differences linked to cooperative or competitive behaviors lead to lower agreements. These results underline the importance of low-level features like TRP to derive a classification of turn changes that would be less subject to interpretation. The analysis of the presence of overlapping speech highlights the existence of interruptions without overlaps and smooth transitions with overlaps. These annotations are available at https://lium.univ-lemans.fr/corpus-allies/.

Annotation of Transition-Relevance Places and Interruptions for the Description of Turn-Taking in Conversations in French Media Content

lrec-main.110

Poster

https://aclanthology.org/2024.lrec-main.111.bib

https://aclanthology.org/2024.lrec-main.111/

@inproceedings{rikters-etal-2024-annotations, title = "Annotations for Exploring Food Tweets from Multiple Aspects", author = "Rikters, Matiss and V{\=\i}ksna, Rinalds and Marrese-Taylor, Edison", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.111", pages = "1233--1238", abstract = "This research builds upon the Latvian Twitter Eater Corpus (LTEC), which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. LTEC has been collected for more than 12 years and reaching almost 3 million tweets with the basic information as well as extended automatically and manually annotated metadata. In this paper we supplement the LTEC with manually annotated subsets of evaluation data for machine translation, named entity recognition, timeline-balanced sentiment analysis, and text-image relation classification. We experiment with each of the data sets using baseline models and highlight future challenges for various modelling approaches.", }

This research builds upon the Latvian Twitter Eater Corpus (LTEC), which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. LTEC has been collected for more than 12 years and reaching almost 3 million tweets with the basic information as well as extended automatically and manually annotated metadata. In this paper we supplement the LTEC with manually annotated subsets of evaluation data for machine translation, named entity recognition, timeline-balanced sentiment analysis, and text-image relation classification. We experiment with each of the data sets using baseline models and highlight future challenges for various modelling approaches.

Annotations for Exploring Food Tweets from Multiple Aspects

lrec-main.111

Poster

https://aclanthology.org/2024.lrec-main.112.bib

https://aclanthology.org/2024.lrec-main.112/

@inproceedings{ignat-etal-2024-annotations, title = "Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost", author = "Ignat, Oana and Bai, Longju and Nwatu, Joan C. and Mihalcea, Rada", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.112", pages = "1239--1259", abstract = "Current foundation models have shown impressive performance across various tasks. However, several studies have revealed that these models are not effective for everyone due to the imbalanced geographical and economic representation of the data used in the training process. Most of this data comes from Western countries, leading to poor results for underrepresented countries. To address this issue, more data needs to be collected from these countries, but the cost of annotation can be a significant bottleneck. In this paper, we propose methods to identify the data to be annotated to balance model performance and annotation costs. Our approach first involves finding the countries with images of topics (objects and actions) most visually distinct from those already in the training datasets used by current large vision-language foundation models. Next, we identify countries with higher visual similarity for these topics and show that using data from these countries to supplement the training data improves model performance and reduces annotation costs. The resulting lists of countries and corresponding topics are made available at https://github.com/MichiganNLP/visual{\_}diversity{\_}budget.", }

Current foundation models have shown impressive performance across various tasks. However, several studies have revealed that these models are not effective for everyone due to the imbalanced geographical and economic representation of the data used in the training process. Most of this data comes from Western countries, leading to poor results for underrepresented countries. To address this issue, more data needs to be collected from these countries, but the cost of annotation can be a significant bottleneck. In this paper, we propose methods to identify the data to be annotated to balance model performance and annotation costs. Our approach first involves finding the countries with images of topics (objects and actions) most visually distinct from those already in the training datasets used by current large vision-language foundation models. Next, we identify countries with higher visual similarity for these topics and show that using data from these countries to supplement the training data improves model performance and reduces annotation costs. The resulting lists of countries and corresponding topics are made available at https://github.com/MichiganNLP/visual{\_}diversity{\_}budget.

Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost

lrec-main.112

Poster

2403.07687

https://aclanthology.org/2024.lrec-main.113.bib

https://aclanthology.org/2024.lrec-main.113/

@inproceedings{acosta-triana-etal-2024-annotheia, title = "{A}nno{T}heia: A Semi-Automatic Annotation Toolkit for Audio-Visual Speech Technologies", author = "Acosta-Triana, Jos{\'e}-M. and Gimeno-G{\'o}mez, David and Mart{\'\i}nez-Hinarejos, Carlos-D.", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.113", pages = "1260--1269", abstract = "More than 7,000 known languages are spoken around the world. However, due to the lack of annotated resources, only a small fraction of them are currently covered by speech technologies. Albeit self-supervised speech representations, recent massive speech corpora collections, as well as the organization of challenges, have alleviated this inequality, most studies are mainly benchmarked on English. This situation is aggravated when tasks involving both acoustic and visual speech modalities are addressed. In order to promote research on low-resource languages for audio-visual speech technologies, we present AnnoTheia, a semi-automatic annotation toolkit that detects when a person speaks on the scene and the corresponding transcription. In addition, to show the complete process of preparing AnnoTheia for a language of interest, we also describe the adaptation of a pre-trained model for active speaker detection to Spanish, using a database not initially conceived for this type of task. Prior evaluations show that the toolkit is able to speed up to four times the annotation process. The AnnoTheia toolkit, tutorials, and pre-trained models are available at https://github.com/joactr/AnnoTheia/.", }

More than 7,000 known languages are spoken around the world. However, due to the lack of annotated resources, only a small fraction of them are currently covered by speech technologies. Albeit self-supervised speech representations, recent massive speech corpora collections, as well as the organization of challenges, have alleviated this inequality, most studies are mainly benchmarked on English. This situation is aggravated when tasks involving both acoustic and visual speech modalities are addressed. In order to promote research on low-resource languages for audio-visual speech technologies, we present AnnoTheia, a semi-automatic annotation toolkit that detects when a person speaks on the scene and the corresponding transcription. In addition, to show the complete process of preparing AnnoTheia for a language of interest, we also describe the adaptation of a pre-trained model for active speaker detection to Spanish, using a database not initially conceived for this type of task. Prior evaluations show that the toolkit is able to speed up to four times the annotation process. The AnnoTheia toolkit, tutorials, and pre-trained models are available at https://github.com/joactr/AnnoTheia/.

AnnoTheia: A Semi-Automatic Annotation Toolkit for Audio-Visual Speech Technologies

lrec-main.113

Poster

2402.13152

https://aclanthology.org/2024.lrec-main.114.bib

https://aclanthology.org/2024.lrec-main.114/

@inproceedings{synkova-etal-2024-announcing, title = "Announcing the {P}rague Discourse Treebank 3.0", author = "Synkov{\'a}, Pavl{\'\i}na and M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i} and Pol{\'a}kov{\'a}, Lucie and Rysov{\'a}, Magdal{\'e}na", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.114", pages = "1270--1279", abstract = "We present the Prague Discourse Treebank 3.0 {--} a new version of the annotation of discourse relations marked by primary and secondary discourse connectives in the data of the Prague Dependency Treebank. Compared to the previous version (PDiT 2.0), the version 3.0 comes with three types of major updates: (i) it brings a largely revised annotation of discourse relations: pragmatic relations have been thoroughly reworked, many inconsistencies across all discourse types have been fixed and previously unclear cases marked in annotators{'} comments have been resolved, (ii) it achieves consistency with a Lexicon of Czech Discourse Connectives (CzeDLex), and (iii) it provides the data not only in its native format (Prague Markup Language, discourse relations annotated at the top of the dependency trees), but also in the Penn Discourse Treebank 3.0 format (plain text plus a stand-off discourse annotation) and sense taxonomy. PDiT 3.0 contains 21,662 discourse relations (plus 445 list relations) in 49 thousand sentences.", }

We present the Prague Discourse Treebank 3.0 {--} a new version of the annotation of discourse relations marked by primary and secondary discourse connectives in the data of the Prague Dependency Treebank. Compared to the previous version (PDiT 2.0), the version 3.0 comes with three types of major updates: (i) it brings a largely revised annotation of discourse relations: pragmatic relations have been thoroughly reworked, many inconsistencies across all discourse types have been fixed and previously unclear cases marked in annotators{'} comments have been resolved, (ii) it achieves consistency with a Lexicon of Czech Discourse Connectives (CzeDLex), and (iii) it provides the data not only in its native format (Prague Markup Language, discourse relations annotated at the top of the dependency trees), but also in the Penn Discourse Treebank 3.0 format (plain text plus a stand-off discourse annotation) and sense taxonomy. PDiT 3.0 contains 21,662 discourse relations (plus 445 list relations) in 49 thousand sentences.

Announcing the Prague Discourse Treebank 3.0

lrec-main.114

Poster

https://aclanthology.org/2024.lrec-main.115.bib

https://aclanthology.org/2024.lrec-main.115/

@inproceedings{park-etal-2024-novel, title = "A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using {BERT}-based Language Models", author = {Park, Namu and Lybarger, Kevin and Ramachandran, Giridhar Kaushik and Lewis, Spencer and Damani, Aashka and Uzuner, {\"O}zlem and Gunn, Martin and Yetisgen, Meliha}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.115", pages = "1280--1292", abstract = "Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions, including oncological, neurological, cardiovascular, and musculoskeletal disorders, among others. Radiologists interpret these complex, unstructured images and articulate their assessments through narrative reports that remain largely unstructured. This unstructured narrative must be converted into a structured semantic representation to facilitate secondary applications such as retrospective analyses or clinical decision support. Here, we introduce the Corpus of Annotated Medical Imaging Reports (CAMIR), which includes 609 annotated radiology reports from three imaging modality types: Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography-Computed Tomography. Reports were annotated using an event-based schema that captures clinical indications, lesions, and medical problems. Each event consists of a trigger and multiple arguments, and a majority of the argument types, including anatomy, normalize the spans to pre-defined concepts to facilitate secondary use. CAMIR uniquely combines a granular event structure and concept normalization. To extract CAMIR events, we explored two BERT (Bi-directional Encoder Representation from Transformers)-based architectures, including an existing architecture (mSpERT) that jointly extracts all event information and a multi-step approach (PL-Marker++) that we augmented for the CAMIR schema.", }

Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions, including oncological, neurological, cardiovascular, and musculoskeletal disorders, among others. Radiologists interpret these complex, unstructured images and articulate their assessments through narrative reports that remain largely unstructured. This unstructured narrative must be converted into a structured semantic representation to facilitate secondary applications such as retrospective analyses or clinical decision support. Here, we introduce the Corpus of Annotated Medical Imaging Reports (CAMIR), which includes 609 annotated radiology reports from three imaging modality types: Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography-Computed Tomography. Reports were annotated using an event-based schema that captures clinical indications, lesions, and medical problems. Each event consists of a trigger and multiple arguments, and a majority of the argument types, including anatomy, normalize the spans to pre-defined concepts to facilitate secondary use. CAMIR uniquely combines a granular event structure and concept normalization. To extract CAMIR events, we explored two BERT (Bi-directional Encoder Representation from Transformers)-based architectures, including an existing architecture (mSpERT) that jointly extracts all event information and a multi-step approach (PL-Marker++) that we augmented for the CAMIR schema.

A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models

lrec-main.115

Poster

2403.18975

https://aclanthology.org/2024.lrec-main.116.bib

https://aclanthology.org/2024.lrec-main.116/

@inproceedings{ji-kong-2024-novel, title = "A Novel Three-stage Framework for Few-shot Named Entity Recognition", author = "Ji, Shengjie and Kong, Fang", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.116", pages = "1293--1305", abstract = "Different from most existing tasks relying on abundant labeled data, Few-shot Named Entity Recognition (NER) aims to develop NER systems that are capable of learning from a small set of labeled samples and then generalizing well to new, unseen data.In this paper, with the intention of obtaining a model that can better adapt to new domains, we design a novel three-stage framework for Few-shot NER, including teacher span recognizer, student span recognizer and entity classifier.We first train a teacher span recognizer which is based on a global boundary matrix to obtain soft boundary labels.Then we leverage the soft boundary labels learned by the teacher model to assist in training the student span recognizer,which can smooth the training process of span recognizer.Finally, we adopt the traditional prototypical network as entity classifier and incorporate the idea of prompt learning to construct a more generalizable semantic space.Extensive experiments on various benchmarks demonstrate that our approach surpasses prior methods.", }

Different from most existing tasks relying on abundant labeled data, Few-shot Named Entity Recognition (NER) aims to develop NER systems that are capable of learning from a small set of labeled samples and then generalizing well to new, unseen data.In this paper, with the intention of obtaining a model that can better adapt to new domains, we design a novel three-stage framework for Few-shot NER, including teacher span recognizer, student span recognizer and entity classifier.We first train a teacher span recognizer which is based on a global boundary matrix to obtain soft boundary labels.Then we leverage the soft boundary labels learned by the teacher model to assist in training the student span recognizer,which can smooth the training process of span recognizer.Finally, we adopt the traditional prototypical network as entity classifier and incorporate the idea of prompt learning to construct a more generalizable semantic space.Extensive experiments on various benchmarks demonstrate that our approach surpasses prior methods.

A Novel Three-stage Framework for Few-shot Named Entity Recognition

lrec-main.116

Poster

https://aclanthology.org/2024.lrec-main.117.bib

https://aclanthology.org/2024.lrec-main.117/

@inproceedings{liu-etal-2024-antcritic, title = "{A}nt{C}ritic: Argument Mining for Free-Form and Visually-Rich Financial Comments", author = "Liu, Huadai and Wenqiang, Xu and Lin, Xuan and Huo, Jingjing and Chen, Hong and Zhao, Zhou", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.117", pages = "1306--1317", abstract = "Argument mining aims to detect all possible argumentative components and identify their relationships automatically. As a thriving task in natural language processing, there has been a large amount of corpus for academic study and application development in this field. However, the research in this area is still constrained by the inherent limitations of existing datasets. Specifically, all the publicly available datasets are relatively small in scale, and few of them provide information from other modalities to facilitate the learning process. Moreover, the statements and expressions in these corpora are usually in a \textit{compact} form, which restricts the generalization ability of models. To this end, we collect a novel dataset \textit{AntCritic} to serve as a helpful complement to this area, which consists of about 10k free-form and visually-rich financial comments and supports both argument component detection and argument relation prediction tasks. Besides, to cope with the challenges brought by scenario expansion, we thoroughly explore the fine-grained relation prediction and structure reconstruction scheme and discuss the encoding mechanism for visual styles and layouts. On this basis, we design two simple but effective model architectures and conduct various experiments on this dataset to provide benchmark performances as a reference and verify the practicability of our proposed architecture. We release our data and code in this \textit{link}, and this dataset follows CC BY-NC-ND 4.0 license.", }

Argument mining aims to detect all possible argumentative components and identify their relationships automatically. As a thriving task in natural language processing, there has been a large amount of corpus for academic study and application development in this field. However, the research in this area is still constrained by the inherent limitations of existing datasets. Specifically, all the publicly available datasets are relatively small in scale, and few of them provide information from other modalities to facilitate the learning process. Moreover, the statements and expressions in these corpora are usually in a \textit{compact} form, which restricts the generalization ability of models. To this end, we collect a novel dataset \textit{AntCritic} to serve as a helpful complement to this area, which consists of about 10k free-form and visually-rich financial comments and supports both argument component detection and argument relation prediction tasks. Besides, to cope with the challenges brought by scenario expansion, we thoroughly explore the fine-grained relation prediction and structure reconstruction scheme and discuss the encoding mechanism for visual styles and layouts. On this basis, we design two simple but effective model architectures and conduct various experiments on this dataset to provide benchmark performances as a reference and verify the practicability of our proposed architecture. We release our data and code in this \textit{link}, and this dataset follows CC BY-NC-ND 4.0 license.

AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments

lrec-main.117

Poster

2208.09612

https://aclanthology.org/2024.lrec-main.118.bib

https://aclanthology.org/2024.lrec-main.118/

@inproceedings{li-etal-2024-unsupervised, title = "An Unsupervised Framework for Adaptive Context-aware Simplified-Traditional {C}hinese Character Conversion", author = "Li, Wei and Huang, Shutan and Shao, Yanqiu", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.118", pages = "1318--1326", abstract = "Traditional Chinese character is an important carrier of Chinese culture, and is still actively used in many areas. Automatic conversion between traditional and simplified Chinese characters can help modern people understand traditional culture and facilitate communication among different regions. Previous conversion methods rely on rule-based mapping or shallow feature-based machine learning models, which struggle to convert simplified characters with different origins and constructing training data is costly. In this study, we propose an unsupervised adaptive context-aware conversion model that learns to convert between simplified and traditional Chinese characters under a denoising auto-encoder framework requiring no labeled data. Our model includes a Latent Generative Adversarial Encoder that transforms vectors to a latent space with generative adversarial network, which adds noise as an inevitable side effect, Based on which a Context-aware Semantic Reconstruction Decoder restores the original input while considering a broader range of context with a pretrained language model. Additionally, we propose to apply early exit mechanism during inference to reduce the computation complexity and improve the generalization ability. To test the effectiveness of our model, we construct a high quality test dataset with simplified-traditional Chinese character text pairs. Experiment results and extensive analysis demonstrate that our model outperforms strong unsupervised baselines and yields better conversion result for one-to-many cases.", }

Traditional Chinese character is an important carrier of Chinese culture, and is still actively used in many areas. Automatic conversion between traditional and simplified Chinese characters can help modern people understand traditional culture and facilitate communication among different regions. Previous conversion methods rely on rule-based mapping or shallow feature-based machine learning models, which struggle to convert simplified characters with different origins and constructing training data is costly. In this study, we propose an unsupervised adaptive context-aware conversion model that learns to convert between simplified and traditional Chinese characters under a denoising auto-encoder framework requiring no labeled data. Our model includes a Latent Generative Adversarial Encoder that transforms vectors to a latent space with generative adversarial network, which adds noise as an inevitable side effect, Based on which a Context-aware Semantic Reconstruction Decoder restores the original input while considering a broader range of context with a pretrained language model. Additionally, we propose to apply early exit mechanism during inference to reduce the computation complexity and improve the generalization ability. To test the effectiveness of our model, we construct a high quality test dataset with simplified-traditional Chinese character text pairs. Experiment results and extensive analysis demonstrate that our model outperforms strong unsupervised baselines and yields better conversion result for one-to-many cases.

An Unsupervised Framework for Adaptive Context-aware Simplified-Traditional Chinese Character Conversion

lrec-main.118

Poster

https://aclanthology.org/2024.lrec-main.119.bib

https://aclanthology.org/2024.lrec-main.119/

@inproceedings{jo-etal-2024-untold, title = "An Untold Story of Preprocessing Task Evaluation: An Alignment-based Joint Evaluation Approach", author = "Jo, Eunkyul Leah and Park, Angela Yoonseo and Zhang, Grace Tianjiao and Wang, Izia Xiaoxiao and Wang, Junrui and Mao, MingJia and Park, Jungyeul", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.119", pages = "1327--1338", abstract = "A preprocessing task such as tokenization and sentence boundary detection (SBD) has commonly been considered as NLP challenges that have already been solved. This perception is due to their generally good performance and the presence of pre-tokenized data. However, it{'}s important to note that the low error rates of current methods are mainly specific to certain tasks, and rule-based tokenization can be difficult to use across different systems. Despite being subtle, these limitations are significant in the context of the NLP pipeline. In this paper, we introduce a novel evaluation algorithm for the preprocessing task, including both tokenization and SBD results. This algorithm aims to enhance the reliability of evaluations by reevaluating the counts of true positive cases for F1 measures in both preprocessing tasks jointly. It achieves this through an alignment-based approach inspired by sentence and word alignments used in machine translation. Our evaluation algorithm not only allows for precise counting of true positive tokens and sentence boundaries but also combines these two evaluation tasks into a single organized pipeline. To illustrate and clarify the intricacies of this calculation and integration, we provide detailed pseudo-code configurations for implementation. Additionally, we offer empirical evidence demonstrating how sentence and word alignment can improve evaluation reliability and present case studies to further support our approach.", }

A preprocessing task such as tokenization and sentence boundary detection (SBD) has commonly been considered as NLP challenges that have already been solved. This perception is due to their generally good performance and the presence of pre-tokenized data. However, it{'}s important to note that the low error rates of current methods are mainly specific to certain tasks, and rule-based tokenization can be difficult to use across different systems. Despite being subtle, these limitations are significant in the context of the NLP pipeline. In this paper, we introduce a novel evaluation algorithm for the preprocessing task, including both tokenization and SBD results. This algorithm aims to enhance the reliability of evaluations by reevaluating the counts of true positive cases for F1 measures in both preprocessing tasks jointly. It achieves this through an alignment-based approach inspired by sentence and word alignments used in machine translation. Our evaluation algorithm not only allows for precise counting of true positive tokens and sentence boundaries but also combines these two evaluation tasks into a single organized pipeline. To illustrate and clarify the intricacies of this calculation and integration, we provide detailed pseudo-code configurations for implementation. Additionally, we offer empirical evidence demonstrating how sentence and word alignment can improve evaluation reliability and present case studies to further support our approach.

An Untold Story of Preprocessing Task Evaluation: An Alignment-based Joint Evaluation Approach

lrec-main.119

Poster

https://aclanthology.org/2024.lrec-main.120.bib

https://aclanthology.org/2024.lrec-main.120/

@inproceedings{lyu-etal-2024-paradigm, title = "A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models", author = "Lyu, Chenyang and Du, Zefeng and Xu, Jitao and Duan, Yitao and Wu, Minghao and Lynn, Teresa and Aji, Alham Fikri and Wong, Derek F. and Wang, Longyue", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.120", pages = "1339--1352", abstract = "Machine Translation (MT) has greatly advanced over the years due to the developments in deep neural networks. However, the emergence of Large Language Models (LLMs) like GPT-4 and ChatGPT is introducing a new phase in the MT domain. In this context, we believe that the future of MT is intricately tied to the capabilities of LLMs. These models not only offer vast linguistic understandings but also bring innovative methodologies, such as prompt-based techniques, that have the potential to further elevate MT. In this paper, we provide an overview of the significant enhancements in MT that are influenced by LLMs and advocate for their pivotal role in upcoming MT research and implementations. We highlight several new MT directions, emphasizing the benefits of LLMs in scenarios such as Long-Document Translation, Stylized Translation, and Interactive Translation. Additionally, we address the important concern of privacy in LLM-driven MT and suggest essential privacy-preserving strategies. By showcasing practical instances, we aim to demonstrate the advantages that LLMs offer, particularly in tasks like translating extended documents. We conclude by emphasizing the critical role of LLMs in guiding the future evolution of MT and offer a roadmap for future exploration in the sector.", }

Machine Translation (MT) has greatly advanced over the years due to the developments in deep neural networks. However, the emergence of Large Language Models (LLMs) like GPT-4 and ChatGPT is introducing a new phase in the MT domain. In this context, we believe that the future of MT is intricately tied to the capabilities of LLMs. These models not only offer vast linguistic understandings but also bring innovative methodologies, such as prompt-based techniques, that have the potential to further elevate MT. In this paper, we provide an overview of the significant enhancements in MT that are influenced by LLMs and advocate for their pivotal role in upcoming MT research and implementations. We highlight several new MT directions, emphasizing the benefits of LLMs in scenarios such as Long-Document Translation, Stylized Translation, and Interactive Translation. Additionally, we address the important concern of privacy in LLM-driven MT and suggest essential privacy-preserving strategies. By showcasing practical instances, we aim to demonstrate the advantages that LLMs offer, particularly in tasks like translating extended documents. We conclude by emphasizing the critical role of LLMs in guiding the future evolution of MT and offer a roadmap for future exploration in the sector.

A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models

lrec-main.120

Poster

2305.01181

https://huggingface.co/papers/2305.01181

https://aclanthology.org/2024.lrec-main.121.bib

https://aclanthology.org/2024.lrec-main.121/

@inproceedings{cunha-etal-2024-persona, title = "A Persona-Based Corpus in the Diabetes Self-Care Domain - Applying a Human-Centered Approach to a Low-Resource Context", author = "Cunha, Rossana and Castro Ferreira, Thiago and Pagano, Adriana and Alves, Fabio", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.121", pages = "1353--1369", abstract = "While Natural Language Processing (NLP) models have gained substantial attention, only in recent years has research opened new paths for tackling Human-Computer Design (HCD) from the perspective of natural language. We focus on developing a human-centered corpus, more specifically, a persona-based corpus in a particular healthcare domain (diabetes mellitus self-care). In order to follow an HCD approach, we created personas to model interpersonal interaction (expert and non-expert users) in that specific domain. We show that an HCD approach benefits language generation from different perspectives, from machines to humans - contributing with new directions for low-resource contexts (languages other than English and sensitive domains) where the need to promote effective communication is essential.", }

While Natural Language Processing (NLP) models have gained substantial attention, only in recent years has research opened new paths for tackling Human-Computer Design (HCD) from the perspective of natural language. We focus on developing a human-centered corpus, more specifically, a persona-based corpus in a particular healthcare domain (diabetes mellitus self-care). In order to follow an HCD approach, we created personas to model interpersonal interaction (expert and non-expert users) in that specific domain. We show that an HCD approach benefits language generation from different perspectives, from machines to humans - contributing with new directions for low-resource contexts (languages other than English and sensitive domains) where the need to promote effective communication is essential.

A Persona-Based Corpus in the Diabetes Self-Care Domain - Applying a Human-Centered Approach to a Low-Resource Context

lrec-main.121

Poster

https://aclanthology.org/2024.lrec-main.122.bib

https://aclanthology.org/2024.lrec-main.122/

@inproceedings{sun-etal-2024-apollo, title = "{APOLLO}: An Optimized Training Approach for Long-form Numerical Reasoning", author = "Sun, Jiashuo and Zhang, Hang and Lin, Chen and Su, Xiangdong and Gong, Yeyun and Guo, Jian", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.122", pages = "1370--1382", abstract = "Long-form numerical reasoning aims to generate a reasoning program to calculate the answer for a given question. Previous work followed a retriever-generator framework, where the retriever selects key facts from a long-form document, and the generator generates a reasoning program based on the retrieved facts. However, they treated all facts equally without considering the different contributions of facts with and without numerical information. Furthermore, they ignored program consistency, leading to the wrong punishment of programs that differed from the ground truth. In order to address these issues, we proposed APOLLO (An optimized training aPproach fOr Long-form numericaL reasOning), to improve long-form numerical reasoning. APOLLO includes a number-aware negative sampling strategy for the retriever to discriminate key numerical facts, and a consistency-based reinforcement learning with target program augmentation for the generator to ultimately increase the execution accuracy. Experimental results on the FinQA and ConvFinQA leaderboards verify the effectiveness of our proposed methods, achieving the new state-of-the-art.", }

Long-form numerical reasoning aims to generate a reasoning program to calculate the answer for a given question. Previous work followed a retriever-generator framework, where the retriever selects key facts from a long-form document, and the generator generates a reasoning program based on the retrieved facts. However, they treated all facts equally without considering the different contributions of facts with and without numerical information. Furthermore, they ignored program consistency, leading to the wrong punishment of programs that differed from the ground truth. In order to address these issues, we proposed APOLLO (An optimized training aPproach fOr Long-form numericaL reasOning), to improve long-form numerical reasoning. APOLLO includes a number-aware negative sampling strategy for the retriever to discriminate key numerical facts, and a consistency-based reinforcement learning with target program augmentation for the generator to ultimately increase the execution accuracy. Experimental results on the FinQA and ConvFinQA leaderboards verify the effectiveness of our proposed methods, achieving the new state-of-the-art.

APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning

lrec-main.122

Poster

2212.07249

https://aclanthology.org/2024.lrec-main.123.bib

https://aclanthology.org/2024.lrec-main.123/

@inproceedings{berger-etal-2024-applying, title = "Applying Transfer Learning to {G}erman Metaphor Prediction", author = "Berger, Maria and Kiwitt, Nieke and Reimann, Sebastian", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.123", pages = "1383--1392", abstract = "This paper presents results in transfer-learning metaphor recognition in German. Starting from an English language corpus annotated for metaphor at the sentence level, and its machine-translation to German, we annotate 1000 sentences of the German part to use it as a Gold standard for two different metaphor prediction setups: i) a sequence labeling set-up (on the token-level), and ii) a classification (based on sentences) setup. We test two transfer leaning approaches: i) a group of transformer models, and ii) a technique that utilizes bilingual embeddings together with an RNN classifier. We find out that the transformer models do moderately in a zero-shot scenario (up to 61{\%} F1 for classification) and the embeddings approaches do not even beat the guessing baseline (36{\%} F1 for classification). We use our Gold data to fine-tune the classification tasks on target-language data achieving up to 90{\%} F1 with both, the multilingual BERT and the bilingual embeddings. We also publish the annotated bilingual corpus.", }

This paper presents results in transfer-learning metaphor recognition in German. Starting from an English language corpus annotated for metaphor at the sentence level, and its machine-translation to German, we annotate 1000 sentences of the German part to use it as a Gold standard for two different metaphor prediction setups: i) a sequence labeling set-up (on the token-level), and ii) a classification (based on sentences) setup. We test two transfer leaning approaches: i) a group of transformer models, and ii) a technique that utilizes bilingual embeddings together with an RNN classifier. We find out that the transformer models do moderately in a zero-shot scenario (up to 61{\%} F1 for classification) and the embeddings approaches do not even beat the guessing baseline (36{\%} F1 for classification). We use our Gold data to fine-tune the classification tasks on target-language data achieving up to 90{\%} F1 with both, the multilingual BERT and the bilingual embeddings. We also publish the annotated bilingual corpus.

Applying Transfer Learning to German Metaphor Prediction

lrec-main.123

Poster

https://aclanthology.org/2024.lrec-main.124.bib

https://aclanthology.org/2024.lrec-main.124/

@inproceedings{lahnala-etal-2024-appraisal, title = "Appraisal Framework for Clinical Empathy: A Novel Application to Breaking Bad News Conversations", author = "Lahnala, Allison Claire and Neuendorf, B{\'e}la and Thomin, Alexander and Welch, Charles and Stibane, Tina and Flek, Lucie", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.124", pages = "1393--1407", abstract = "Empathy is essential in healthcare communication. We introduce an annotation approach that draws on well-established frameworks for \textit{clinical empathy} and \textit{breaking bad news} (BBN) conversations for considering the interactive dynamics of discourse relations. We construct Empathy in BBNs, a span-relation task dataset of simulated BBN conversations in German, using our annotation scheme, in collaboration with a large medical school to support research on educational tools for medical didactics. The annotation is based on 1) Pounds (2011){'}s appraisal framework for clinical empathy, which is grounded in systemic functional linguistics, and 2) the SPIKES protocol for breaking bad news (Baile et al., 2000), commonly taught in medical didactics training. This approach presents novel opportunities to study clinical empathic behavior and enables the training of models to detect causal relations involving empathy, a highly desirable feature of systems that can provide feedback to medical professionals in training. We present illustrative examples, discuss applications of the annotation scheme, and insights we can draw from the framework.", }

Empathy is essential in healthcare communication. We introduce an annotation approach that draws on well-established frameworks for \textit{clinical empathy} and \textit{breaking bad news} (BBN) conversations for considering the interactive dynamics of discourse relations. We construct Empathy in BBNs, a span-relation task dataset of simulated BBN conversations in German, using our annotation scheme, in collaboration with a large medical school to support research on educational tools for medical didactics. The annotation is based on 1) Pounds (2011){'}s appraisal framework for clinical empathy, which is grounded in systemic functional linguistics, and 2) the SPIKES protocol for breaking bad news (Baile et al., 2000), commonly taught in medical didactics training. This approach presents novel opportunities to study clinical empathic behavior and enables the training of models to detect causal relations involving empathy, a highly desirable feature of systems that can provide feedback to medical professionals in training. We present illustrative examples, discuss applications of the annotation scheme, and insights we can draw from the framework.

Appraisal Framework for Clinical Empathy: A Novel Application to Breaking Bad News Conversations

lrec-main.124

Poster

https://aclanthology.org/2024.lrec-main.125.bib

https://aclanthology.org/2024.lrec-main.125/

@inproceedings{song-liu-2024-approaches, title = "Approaches and Challenges for Resolving Different Representations of Fictional Characters for {C}hinese Novels", author = "Song, Li and Liu, Ying", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.125", pages = "1408--1421", abstract = "Due to the huge scale of literary works, automatic text analysis technologies are urgently needed for literary studies such as Digital Humanities. However, the domain-generality of existing NLP technologies limits their effectiveness on in-depth literary studies. It is valuable to explore how to adapt NLP technologies to the literary-specific tasks. Fictional characters are the most essential elements of a novel, and thus crucial to understanding the content of novels. The prerequisite of collecting a character{'}s information is to resolve its different representations. It is a specific problem of anaphora resolution which is a classical and open-domain NLP task. We adapt a state-of-the-art anaphora resolution model to resolve character representations in Chinese novels by making some modifications, and train a widely used BERT fine-tuned model for speaker extraction as assistance. We also analyze the challenges and potential solutions for character-resolution in Chinese novels according to the resolution results on a specific Chinese novel.", }

Due to the huge scale of literary works, automatic text analysis technologies are urgently needed for literary studies such as Digital Humanities. However, the domain-generality of existing NLP technologies limits their effectiveness on in-depth literary studies. It is valuable to explore how to adapt NLP technologies to the literary-specific tasks. Fictional characters are the most essential elements of a novel, and thus crucial to understanding the content of novels. The prerequisite of collecting a character{'}s information is to resolve its different representations. It is a specific problem of anaphora resolution which is a classical and open-domain NLP task. We adapt a state-of-the-art anaphora resolution model to resolve character representations in Chinese novels by making some modifications, and train a widely used BERT fine-tuned model for speaker extraction as assistance. We also analyze the challenges and potential solutions for character-resolution in Chinese novels according to the resolution results on a specific Chinese novel.

Approaches and Challenges for Resolving Different Representations of Fictional Characters for Chinese Novels

lrec-main.125

Poster

https://aclanthology.org/2024.lrec-main.126.bib

https://aclanthology.org/2024.lrec-main.126/

@inproceedings{madina-etal-2024-preliminary, title = "A Preliminary Study of {C}hat{GPT} for {S}panish {E}2{R} Text Adaptation", author = "Madina, Margot and Gonzalez-Dios, Itziar and Siegel, Melanie", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.126", pages = "1422--1434", abstract = "The process of adapting and creating Easy-to-Read (E2R) texts is very expensive and time-consuming. Due to the success of Large Language Models (LLMs) such as ChatGPT and their ability to generate written language, it is likely to think that such models can help in the adaptation or creation of text in E2R. In this paper, we explore the concept of E2R, its underlying principles and applications, and provides a preliminary study on the usefulness of ChatGPT-4 for E2R text adaptation. We focus on the Spanish language and its E2R variant, Lectura F{\'a}cil (LF). We consider a range of prompts that can be used and the differences in output that this produces. We then carry out a three-folded evaluation on 10 texts adapted by ChatGPT-4: (1) an automated evaluation to check values related to the readability of texts, (2) a checklist-based manual evaluation (for which we also propose three new capabilities) and (3) a users{'} evaluation with people with cognitive disabilities. We show that it is difficult to choose the best prompt to make ChatGPT-4 adapt texts to LF. Furthermore, the generated output does not follow the E2R text rules, so it is often not suitable for the target audience.", }

The process of adapting and creating Easy-to-Read (E2R) texts is very expensive and time-consuming. Due to the success of Large Language Models (LLMs) such as ChatGPT and their ability to generate written language, it is likely to think that such models can help in the adaptation or creation of text in E2R. In this paper, we explore the concept of E2R, its underlying principles and applications, and provides a preliminary study on the usefulness of ChatGPT-4 for E2R text adaptation. We focus on the Spanish language and its E2R variant, Lectura F{\'a}cil (LF). We consider a range of prompts that can be used and the differences in output that this produces. We then carry out a three-folded evaluation on 10 texts adapted by ChatGPT-4: (1) an automated evaluation to check values related to the readability of texts, (2) a checklist-based manual evaluation (for which we also propose three new capabilities) and (3) a users{'} evaluation with people with cognitive disabilities. We show that it is difficult to choose the best prompt to make ChatGPT-4 adapt texts to LF. Furthermore, the generated output does not follow the E2R text rules, so it is often not suitable for the target audience.

A Preliminary Study of ChatGPT for Spanish E2R Text Adaptation

lrec-main.126

Poster

https://aclanthology.org/2024.lrec-main.127.bib

https://aclanthology.org/2024.lrec-main.127/

@inproceedings{qiao-etal-2024-quantum, title = "A Quantum-Inspired Matching Network with Linguistic Theories for Metaphor Detection", author = "Qiao, Wenbo and Zhang, Peng and Ma, ZengLai", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.127", pages = "1435--1445", abstract = "Enabling machines with the capability to recognize and comprehend metaphors is a crucial step toward achieving artificial intelligence. In linguistic theories, metaphor can be identified through Metaphor Identification Procedure (MIP) or Selectional Preference Violation (SPV), both of which are typically considered as matching tasks in the field of natural language processing. However, the implementation of MIP poses a challenge due to the semantic uncertainty and ambiguity of literal meanings of words. Simultaneously, SPV often struggles to recognize conventional metaphors. Inspired by Quantum Language Model (QLM) for modeling semantic uncertainty and fine-grained feature matching, we propose a quantum-inspired matching network for metaphor detection. Specifically, we use the density matrix to explicitly characterize the literal meanings of the target word for MIP, in order to model the uncertainty and ambiguity of the literal meanings of words. This can make SPV effective even in the face of conventional metaphors. MIP and SPV are then achieved by fine-grained feature matching. The results of the experiment finally demonstrated our approach has strong competitiveness.", }

Enabling machines with the capability to recognize and comprehend metaphors is a crucial step toward achieving artificial intelligence. In linguistic theories, metaphor can be identified through Metaphor Identification Procedure (MIP) or Selectional Preference Violation (SPV), both of which are typically considered as matching tasks in the field of natural language processing. However, the implementation of MIP poses a challenge due to the semantic uncertainty and ambiguity of literal meanings of words. Simultaneously, SPV often struggles to recognize conventional metaphors. Inspired by Quantum Language Model (QLM) for modeling semantic uncertainty and fine-grained feature matching, we propose a quantum-inspired matching network for metaphor detection. Specifically, we use the density matrix to explicitly characterize the literal meanings of the target word for MIP, in order to model the uncertainty and ambiguity of the literal meanings of words. This can make SPV effective even in the face of conventional metaphors. MIP and SPV are then achieved by fine-grained feature matching. The results of the experiment finally demonstrated our approach has strong competitiveness.

A Quantum-Inspired Matching Network with Linguistic Theories for Metaphor Detection

lrec-main.127

Poster

https://aclanthology.org/2024.lrec-main.128.bib

https://aclanthology.org/2024.lrec-main.128/

@inproceedings{elmallah-etal-2024-arabic, title = "{A}rabic Diacritization Using Morphologically Informed Character-Level Model", author = "Elmallah, Muhammad Morsy and Reda, Mahmoud and Darwish, Kareem and El-Sheikh, Abdelrahman and Elneima, Ashraf Hatim and Aljubran, Murtadha and Alsaeed, Nouf and Mohammed, Reem and Al-Badrashiny, Mohamed", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.128", pages = "1446--1454", abstract = "Arabic diacritic recovery i.e. diacritization is necessary for proper vocalization and an enabler for downstream applications such as language learning and text to speech. Diacritics come in two varieties, namely: core-word diacritics and case endings. In this paper we introduce a highly effective morphologically informed character-level model that can recover both types of diacritics simultaneously. The model uses a Recurrent Neural Network (RNN) based architecture that takes in text as a sequence of characters, with markers for morphological segmentation, and outputs a sequence of diacritics. We also introduce a character-based morphological segmentation model that we train for Modern Standard Arabic (MSA) and dialectal Arabic. We demonstrate the efficacy of our diacritization model on Classical Arabic, MSA, and two dialectal (Moroccan and Tunisian) texts. We achieve the lowest reported word-level diacritization error rate for MSA (3.4{\%}), match the best results for Classical Arabic (5.4{\%}), and report competitive results for dialectal Arabic.", }

Arabic diacritic recovery i.e. diacritization is necessary for proper vocalization and an enabler for downstream applications such as language learning and text to speech. Diacritics come in two varieties, namely: core-word diacritics and case endings. In this paper we introduce a highly effective morphologically informed character-level model that can recover both types of diacritics simultaneously. The model uses a Recurrent Neural Network (RNN) based architecture that takes in text as a sequence of characters, with markers for morphological segmentation, and outputs a sequence of diacritics. We also introduce a character-based morphological segmentation model that we train for Modern Standard Arabic (MSA) and dialectal Arabic. We demonstrate the efficacy of our diacritization model on Classical Arabic, MSA, and two dialectal (Moroccan and Tunisian) texts. We achieve the lowest reported word-level diacritization error rate for MSA (3.4{\%}), match the best results for Classical Arabic (5.4{\%}), and report competitive results for dialectal Arabic.

Arabic Diacritization Using Morphologically Informed Character-Level Model

lrec-main.128

Poster

https://aclanthology.org/2024.lrec-main.129.bib

https://aclanthology.org/2024.lrec-main.129/

@inproceedings{fang-etal-2024-arbitrary, title = "Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding", author = "Fang, Zhiyu and Qin, Jingyan and Zhu, Xiaobin and Yang, Chun and Yin, Xu-Cheng", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.129", pages = "1455--1465", abstract = "Distinguished from traditional knowledge graphs (KGs), temporal knowledge graphs (TKGs) must explore and reason over temporally evolving facts adequately. However, existing TKG approaches still face two main challenges, i.e., the limited capability to model arbitrary timestamps continuously and the lack of rich inference patterns under temporal constraints. In this paper, we propose an innovative TKGE method (PTBox) via polynomial decomposition-based temporal representation and box embedding-based entity representation to tackle the above-mentioned problems. Specifically, we decompose time information by polynomials and then enhance the model{'}s capability to represent arbitrary timestamps flexibly by incorporating the learnable temporal basis tensor. In addition, we model every entity as a hyperrectangle box and define each relation as a transformation on the head and tail entity boxes. The entity boxes can capture complex geometric structures and learn robust representations, improving the model{'}s inductive capability for rich inference patterns. Theoretically, our PTBox can encode arbitrary time information or even unseen timestamps while capturing rich inference patterns and higher-arity relations of the knowledge base. Extensive experiments on real-world datasets demonstrate the effectiveness of our method.", }

Distinguished from traditional knowledge graphs (KGs), temporal knowledge graphs (TKGs) must explore and reason over temporally evolving facts adequately. However, existing TKG approaches still face two main challenges, i.e., the limited capability to model arbitrary timestamps continuously and the lack of rich inference patterns under temporal constraints. In this paper, we propose an innovative TKGE method (PTBox) via polynomial decomposition-based temporal representation and box embedding-based entity representation to tackle the above-mentioned problems. Specifically, we decompose time information by polynomials and then enhance the model{'}s capability to represent arbitrary timestamps flexibly by incorporating the learnable temporal basis tensor. In addition, we model every entity as a hyperrectangle box and define each relation as a transformation on the head and tail entity boxes. The entity boxes can capture complex geometric structures and learn robust representations, improving the model{'}s inductive capability for rich inference patterns. Theoretically, our PTBox can encode arbitrary time information or even unseen timestamps while capturing rich inference patterns and higher-arity relations of the knowledge base. Extensive experiments on real-world datasets demonstrate the effectiveness of our method.

Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding

lrec-main.129

Poster

2405.00358

https://aclanthology.org/2024.lrec-main.130.bib

https://aclanthology.org/2024.lrec-main.130/

@inproceedings{grobol-jouitteau-2024-arbres, title = "{ARBRES} Kenstur: A {B}reton-{F}rench Parallel Corpus Rooted in Field Linguistics", author = {Grobol, Lo{\"\i}c and Jouitteau, M{\'e}lanie}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.130", pages = "1466--1471", abstract = "ARBRES is an ongoing project of open science implemented as a platform ({``}wikigrammar{''}) documenting both the Breton language itself and the state of research and engineering work in linguistics and NLP. Along its nearly 15 years of operation, it has aggregated a wealth of linguistic data in the form of interlinear glosses with translations illustrating lexical items, grammatical features, dialectal variations... While these glosses were primarily meant for human consumption, their volume and the regular format imposed by the wiki engine used for the website also make them suitable for machine processing. ARBRES Kenstur is a new parallel corpus derived from the glosses in ARBRES, including about 5k phrases and sentences in Breton along with translations in standard French. The nature of the original data {---} sourced from field linguistic inquiries meant to document the structure of Breton {---} leads to a resource that is mechanically more concerned with the internal variations of the language and rare phenomena than typical parallel corpora. Preliminaries experiments in using this corpus show that it can help improve machine translation for Breton, demonstrating that sourcing data from field linguistic documentation can be a way to help provide NLP tools for minority and low-resource languages.", }

ARBRES is an ongoing project of open science implemented as a platform ({``}wikigrammar{''}) documenting both the Breton language itself and the state of research and engineering work in linguistics and NLP. Along its nearly 15 years of operation, it has aggregated a wealth of linguistic data in the form of interlinear glosses with translations illustrating lexical items, grammatical features, dialectal variations... While these glosses were primarily meant for human consumption, their volume and the regular format imposed by the wiki engine used for the website also make them suitable for machine processing. ARBRES Kenstur is a new parallel corpus derived from the glosses in ARBRES, including about 5k phrases and sentences in Breton along with translations in standard French. The nature of the original data {---} sourced from field linguistic inquiries meant to document the structure of Breton {---} leads to a resource that is mechanically more concerned with the internal variations of the language and rare phenomena than typical parallel corpora. Preliminaries experiments in using this corpus show that it can help improve machine translation for Breton, demonstrating that sourcing data from field linguistic documentation can be a way to help provide NLP tools for minority and low-resource languages.

ARBRES Kenstur: A Breton-French Parallel Corpus Rooted in Field Linguistics

lrec-main.130

Poster

https://aclanthology.org/2024.lrec-main.131.bib

https://aclanthology.org/2024.lrec-main.131/

@inproceedings{chen-etal-2024-regularization, title = "A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder", author = "Chen, Kedi and Zhou, Jie and Chen, Qin and Liu, Shunyu and He, Liang", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.131", pages = "1472--1485", abstract = "Information extraction (IE) aims to extract complex structured information from the text. Numerous datasets have been constructed for various IE tasks, leading to time-consuming and labor-intensive data annotations. Nevertheless, most prevailing methods focus on training task-specific models, while the common knowledge among different IE tasks is not explicitly modeled. Moreover, the same phrase may have inconsistent labels in different tasks, which poses a big challenge for knowledge transfer using a unified model. In this study, we propose a regularization-based transfer learning method for IE (TIE) via an instructed graph decoder. Specifically, we first construct an instruction pool for datasets from all well-known IE tasks, and then present an instructed graph decoder, which decodes various complex structures into a graph uniformly based on corresponding instructions. In this way, the common knowledge shared with existing datasets can be learned and transferred to a new dataset with new labels. Furthermore, to alleviate the label inconsistency problem among various IE tasks, we introduce a task-specific regularization strategy, which does not update the gradients of two tasks with {`}opposite direction{'}. We conduct extensive experiments on 12 datasets spanning four IE tasks, and the results demonstrate the great advantages of our proposed method.", }

Information extraction (IE) aims to extract complex structured information from the text. Numerous datasets have been constructed for various IE tasks, leading to time-consuming and labor-intensive data annotations. Nevertheless, most prevailing methods focus on training task-specific models, while the common knowledge among different IE tasks is not explicitly modeled. Moreover, the same phrase may have inconsistent labels in different tasks, which poses a big challenge for knowledge transfer using a unified model. In this study, we propose a regularization-based transfer learning method for IE (TIE) via an instructed graph decoder. Specifically, we first construct an instruction pool for datasets from all well-known IE tasks, and then present an instructed graph decoder, which decodes various complex structures into a graph uniformly based on corresponding instructions. In this way, the common knowledge shared with existing datasets can be learned and transferred to a new dataset with new labels. Furthermore, to alleviate the label inconsistency problem among various IE tasks, we introduce a task-specific regularization strategy, which does not update the gradients of two tasks with {`}opposite direction{'}. We conduct extensive experiments on 12 datasets spanning four IE tasks, and the results demonstrate the great advantages of our proposed method.

A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder

lrec-main.131

Poster

2403.00891

https://aclanthology.org/2024.lrec-main.132.bib

https://aclanthology.org/2024.lrec-main.132/

@inproceedings{zhang-etal-2024-reinforcement, title = "A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data", author = "Zhang, Hongxiao and Liu, Mingtong and Li, Chunyou and Chen, Yufeng and Xu, Jinan and Zhou, Ming", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.132", pages = "1486--1497", abstract = "Due to the lack of parallel data, the mainstream fine-tuning-based domain adaptation methods have the overfitting problem in the translation of low-resource domains, and it is difficult for the model to learn the in-domain generalization knowledge. To address the above issue, in this work, we propose a novel Reinforcement Learning Domain Adaptation method for Neural Machine Translation (RLDA-NMT) in the low-resource domain. RLDA-NMT utilizes in-domain source monolingual data to make up for the lack of parallel data, and reinforces domain features learning to make the translation model learn the domain-specific knowledge more fully. Specifically, we first train a ranking-based model with a small-scale in-domain parallel corpus, and then adopt it as the reward model to select higher-quality generated translations for reinforcement when fine-tuning pre-trained NMT model using in-domain source monolingual data. We conduct experiments on Education, Laws, Thesis, and Patent domains of Chinese⇔English translation tasks. Experimental results demonstrate that RLDA-NMT can alleviate overfitting and reinforce the NMT model to learn domain-specific knowledge. Additionally, the results also show that RLDA-NMT and back-translation (BT) are nicely complementary to each other, where combining RLDA-NMT with BT can further improve translation quality.", }

Due to the lack of parallel data, the mainstream fine-tuning-based domain adaptation methods have the overfitting problem in the translation of low-resource domains, and it is difficult for the model to learn the in-domain generalization knowledge. To address the above issue, in this work, we propose a novel Reinforcement Learning Domain Adaptation method for Neural Machine Translation (RLDA-NMT) in the low-resource domain. RLDA-NMT utilizes in-domain source monolingual data to make up for the lack of parallel data, and reinforces domain features learning to make the translation model learn the domain-specific knowledge more fully. Specifically, we first train a ranking-based model with a small-scale in-domain parallel corpus, and then adopt it as the reward model to select higher-quality generated translations for reinforcement when fine-tuning pre-trained NMT model using in-domain source monolingual data. We conduct experiments on Education, Laws, Thesis, and Patent domains of Chinese⇔English translation tasks. Experimental results demonstrate that RLDA-NMT can alleviate overfitting and reinforce the NMT model to learn domain-specific knowledge. Additionally, the results also show that RLDA-NMT and back-translation (BT) are nicely complementary to each other, where combining RLDA-NMT with BT can further improve translation quality.

A Reinforcement Learning Approach to Improve Low-Resource Machine Translation Leveraging Domain Monolingual Data

lrec-main.132

Poster

https://aclanthology.org/2024.lrec-main.133.bib

https://aclanthology.org/2024.lrec-main.133/

@inproceedings{moskvoretskii-etal-2024-large, title = "Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning", author = "Moskvoretskii, Viktor and Panchenko, Alexander and Nikishina, Irina", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.133", pages = "1498--1510", abstract = "Recent studies on LLMs do not pay enough attention to linguistic and lexical semantic tasks, such as taxonomy learning. In this paper, we explore the capacities of Large Language Models featuring LLaMA-2 and Mistral for several Taxonomy-related tasks. We introduce a new methodology and algorithm for data collection via stochastic graph traversal leading to controllable data collection. Collected cases provide the ability to form nearly any type of graph operation. We test the collected dataset for learning taxonomy structure based on English WordNet and compare different input templates for fine-tuning LLMs. Moreover, we apply the fine-tuned models on such datasets on the downstream tasks achieving state-of-the-art results on the TexEval-2 dataset.", }

Recent studies on LLMs do not pay enough attention to linguistic and lexical semantic tasks, such as taxonomy learning. In this paper, we explore the capacities of Large Language Models featuring LLaMA-2 and Mistral for several Taxonomy-related tasks. We introduce a new methodology and algorithm for data collection via stochastic graph traversal leading to controllable data collection. Collected cases provide the ability to form nearly any type of graph operation. We test the collected dataset for learning taxonomy structure based on English WordNet and compare different input templates for fine-tuning LLMs. Moreover, we apply the fine-tuned models on such datasets on the downstream tasks achieving state-of-the-art results on the TexEval-2 dataset.

Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning

lrec-main.133

Poster

https://aclanthology.org/2024.lrec-main.134.bib

https://aclanthology.org/2024.lrec-main.134/

@inproceedings{barriere-cifuentes-2024-text, title = "Are Text Classifiers Xenophobic? A Country-Oriented Bias Detection Method with Least Confounding Variables", author = "Barriere, Valentin and Cifuentes, Sebastian", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.134", pages = "1511--1518", abstract = "Classical bias detection methods used in Machine Learning are themselves biased because of the different confounding variables implied in the assessment of the initial biases. First they are using templates that are syntactically simple and distant from the target data on which the model will deployed. Second, current methods are assessing biases in pre-trained language models or in dataset, but not directly on the fine-tuned classifier that can actually produce harms. We propose a simple method to detect the biases of a specific fine-tuned classifier on any type of unlabeled data. The idea is to study the classifier behavior by creating counterfactual examples directly on the target data distribution and quantify the amount of changes. In this work, we focus on named entity perturbations by applying a Named Entity Recognition on target-domain data and modifying them accordingly to most common names or location of a target group (gender and country), and this for several morphosynctactically different languages spoken in relation with the countries of the target groups. We used our method on two models available open-source that are likely to be deployed by industry, and on two tasks and domains. We first assess the bias of a multilingual sentiment analysis model trained over multiple-languages tweets and available open-source, and then a multilingual stance recognition model trained over several languages and assessed over English language. Finally we propose to link the perplexity of each example with the bias of the model, by looking at the change in label distribution with respect to the language of the target group. Our work offers a fine-grained analysis of the interactions between names and languages, revealing significant biases in multilingual models.", }

Classical bias detection methods used in Machine Learning are themselves biased because of the different confounding variables implied in the assessment of the initial biases. First they are using templates that are syntactically simple and distant from the target data on which the model will deployed. Second, current methods are assessing biases in pre-trained language models or in dataset, but not directly on the fine-tuned classifier that can actually produce harms. We propose a simple method to detect the biases of a specific fine-tuned classifier on any type of unlabeled data. The idea is to study the classifier behavior by creating counterfactual examples directly on the target data distribution and quantify the amount of changes. In this work, we focus on named entity perturbations by applying a Named Entity Recognition on target-domain data and modifying them accordingly to most common names or location of a target group (gender and country), and this for several morphosynctactically different languages spoken in relation with the countries of the target groups. We used our method on two models available open-source that are likely to be deployed by industry, and on two tasks and domains. We first assess the bias of a multilingual sentiment analysis model trained over multiple-languages tweets and available open-source, and then a multilingual stance recognition model trained over several languages and assessed over English language. Finally we propose to link the perplexity of each example with the bias of the model, by looking at the change in label distribution with respect to the language of the target group. Our work offers a fine-grained analysis of the interactions between names and languages, revealing significant biases in multilingual models.

Are Text Classifiers Xenophobic? A Country-Oriented Bias Detection Method with Least Confounding Variables

lrec-main.134

Poster

https://aclanthology.org/2024.lrec-main.135.bib

https://aclanthology.org/2024.lrec-main.135/

@inproceedings{wachsmuth-etal-2024-argument, title = "Argument Quality Assessment in the Age of Instruction-Following Large Language Models", author = "Wachsmuth, Henning and Lapesa, Gabriella and Cabrio, Elena and Lauscher, Anne and Park, Joonsuk and Vecchi, Eva Maria and Villata, Serena and Ziegenbein, Timon", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.135", pages = "1519--1538", abstract = "The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument{'}s quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby.", }

The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument{'}s quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby.

Argument Quality Assessment in the Age of Instruction-Following Large Language Models

lrec-main.135

Poster

2403.16084

https://aclanthology.org/2024.lrec-main.136.bib

https://aclanthology.org/2024.lrec-main.136/

@inproceedings{ly-etal-2024-article, title = "Article Classification with Graph Neural Networks and Multigraphs", author = "Ly, Khang and Kashnitsky, Yury and Chamezopoulos, Savvas and Krzhizhanovskaya, Valeria", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.136", pages = "1539--1547", abstract = "Classifying research output into context-specific label taxonomies is a challenging and relevant downstream task, given the volume of existing and newly published articles. We propose a method to enhance the performance of article classification by enriching simple Graph Neural Network (GNN) pipelines with multi-graph representations that simultaneously encode multiple signals of article relatedness, e.g. references, co-authorship, shared publication source, shared subject headings, as distinct edge types. Fully supervised transductive node classification experiments are conducted on the Open Graph Benchmark OGBN-arXiv dataset and the PubMed diabetes dataset, augmented with additional metadata from Microsoft Academic Graph and PubMed Central, respectively. The results demonstrate that multi-graphs consistently improve the performance of a variety of GNN models compared to the default graphs. When deployed with SOTA textual node embedding methods, the transformed multi-graphs enable simple and shallow 2-layer GNN pipelines to achieve results on par with more complex architectures.", }

Classifying research output into context-specific label taxonomies is a challenging and relevant downstream task, given the volume of existing and newly published articles. We propose a method to enhance the performance of article classification by enriching simple Graph Neural Network (GNN) pipelines with multi-graph representations that simultaneously encode multiple signals of article relatedness, e.g. references, co-authorship, shared publication source, shared subject headings, as distinct edge types. Fully supervised transductive node classification experiments are conducted on the Open Graph Benchmark OGBN-arXiv dataset and the PubMed diabetes dataset, augmented with additional metadata from Microsoft Academic Graph and PubMed Central, respectively. The results demonstrate that multi-graphs consistently improve the performance of a variety of GNN models compared to the default graphs. When deployed with SOTA textual node embedding methods, the transformed multi-graphs enable simple and shallow 2-layer GNN pipelines to achieve results on par with more complex architectures.

Article Classification with Graph Neural Networks and Multigraphs

lrec-main.136

Poster

2309.11341

https://aclanthology.org/2024.lrec-main.137.bib

https://aclanthology.org/2024.lrec-main.137/

@inproceedings{yuan-etal-2024-art, title = "{ART}: The Alternating Reading Task Corpus for Speech Entrainment and Imitation", author = {Yuan, Zheng Byron and de Jong, Dorina and Feng, Ruitao and Be{\v{n}}u{\v{s}}, {\v{S}}tefan and Nguyen, No{\"e}l and Sabo, R{\'o}bert and Fadiga, Luciano and D{'}Ausilio, Alessandro}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.137", pages = "1548--1562", abstract = "We introduce the Alternating Reading Task (ART) Corpus, a collection of dyadic sentence reading for studying the entrainment and imitation behaviour in speech communication. The ART corpus features three experimental conditions - solo reading, alternating reading, and deliberate imitation - as well as three subcorpora encompassing French-, Italian-, and Slovak-accented English. This design allows systematic investigation of speech entrainment in a controlled and less spontaneous setting. Alongside detailed transcriptions, it includes English proficiency scores, demographics, and in-experiment questionnaires for probing linguistic, personal and interpersonal influences on entrainment. Our presentation covers its design, collection, annotation processes, initial analysis, and future research prospects.", }

We introduce the Alternating Reading Task (ART) Corpus, a collection of dyadic sentence reading for studying the entrainment and imitation behaviour in speech communication. The ART corpus features three experimental conditions - solo reading, alternating reading, and deliberate imitation - as well as three subcorpora encompassing French-, Italian-, and Slovak-accented English. This design allows systematic investigation of speech entrainment in a controlled and less spontaneous setting. Alongside detailed transcriptions, it includes English proficiency scores, demographics, and in-experiment questionnaires for probing linguistic, personal and interpersonal influences on entrainment. Our presentation covers its design, collection, annotation processes, initial analysis, and future research prospects.

ART: The Alternating Reading Task Corpus for Speech Entrainment and Imitation

lrec-main.137

Poster

2404.02710

https://aclanthology.org/2024.lrec-main.138.bib

https://aclanthology.org/2024.lrec-main.138/

@inproceedings{ma-etal-2024-self, title = "A Self-verified Method for Exploring Simile Knowledge from Pre-trained Language Models", author = "Ma, Longxuan and Ke, Changxin and Zhou, Shuhan and Sun, Churui and Zhang, Wei-Nan and Liu, Ting", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.138", pages = "1563--1576", abstract = "Simile tasks are challenging in natural language processing (NLP) because models require adequate world knowledge to produce predictions. In recent years, pre-trained language models (PLMs) have succeeded in NLP since they learn generic knowledge from a large corpus. The knowledge embedded in PLMs can be used for different kinds of Simile tasks. However, previous work usually explored one type of simile knowledge for a specific simile task, how to fully utilize different types of knowledge embedded in the PLMs requires further exploration. This paper proposes a self-verified method for exploring simile knowledge from PLMs, which allows the PLMs to leverage one type of simile knowledge to self-validate another. To this end, we first enhance PLMs with a novel multi-level simile recognition (MLSR) task that trains PLMs to evaluate the quality of similes. Then the PLMs leverage this evaluation score to assist the simile interpretation and generation tasks. In this way, we connect different types of simile knowledge in PLMs and make better use of them. Experiments on different pre-trained models and multiple publicly available datasets show that our method works for different kinds of PLMs and can explore more accurate simile knowledge for PLMs. Our code/data will be released on GitHub.", }

Simile tasks are challenging in natural language processing (NLP) because models require adequate world knowledge to produce predictions. In recent years, pre-trained language models (PLMs) have succeeded in NLP since they learn generic knowledge from a large corpus. The knowledge embedded in PLMs can be used for different kinds of Simile tasks. However, previous work usually explored one type of simile knowledge for a specific simile task, how to fully utilize different types of knowledge embedded in the PLMs requires further exploration. This paper proposes a self-verified method for exploring simile knowledge from PLMs, which allows the PLMs to leverage one type of simile knowledge to self-validate another. To this end, we first enhance PLMs with a novel multi-level simile recognition (MLSR) task that trains PLMs to evaluate the quality of similes. Then the PLMs leverage this evaluation score to assist the simile interpretation and generation tasks. In this way, we connect different types of simile knowledge in PLMs and make better use of them. Experiments on different pre-trained models and multiple publicly available datasets show that our method works for different kinds of PLMs and can explore more accurate simile knowledge for PLMs. Our code/data will be released on GitHub.

A Self-verified Method for Exploring Simile Knowledge from Pre-trained Language Models

lrec-main.138

Poster

https://aclanthology.org/2024.lrec-main.139.bib

https://aclanthology.org/2024.lrec-main.139/

@inproceedings{zhang-etal-2024-semantic, title = "A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction", author = "Zhang, Jian and Yang, Changlin and Zhu, Haiping and Lin, Qika and Xu, Fangzhi and Liu, Jun", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.139", pages = "1577--1587", abstract = "Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions; b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensemble graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance.", }

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions; b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensemble graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance.

A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction

lrec-main.139

Poster

https://aclanthology.org/2024.lrec-main.140.bib

https://aclanthology.org/2024.lrec-main.140/

@inproceedings{hamad-etal-2024-asem, title = "{ASEM}: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling", author = "Hamad, Omama and Shaban, Khaled and Hamdi, Ali", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.140", pages = "1588--1601", abstract = "Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks. However, current approaches suffer from several drawbacks, such as the inability to capture the deep semantics of language and sensitivity to minor input variations, resulting in significant changes in the generated text. In this paper, we present a novel solution to these challenges by employing a mixture of experts, multiple encoders, to offer distinct perspectives on the emotional state of the user{'}s utterance while simultaneously enhancing performance. We propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots, enabling the generation of empathetic responses that are fluent and relevant. In contrast to traditional attention mechanisms, the proposed model employs a specialized attention strategy that uniquely zeroes in on sentiment and emotion nuances within the user{'}s utterance. This ensures the generation of context-rich representations tailored to the underlying emotional tone and sentiment intricacies of the text. Our approach outperforms existing methods for generating empathetic embeddings, providing empathetic and diverse responses. The performance of our proposed model significantly exceeds that of existing models, enhancing emotion detection accuracy by 6.2{\%} and lexical diversity by 1.4{\%}. ASEM code is released at https://github.com/MIRAH-Official/Empathetic-Chatbot-ASEM.git", }

Effective feature representations play a critical role in enhancing the performance of text generation models that rely on deep neural networks. However, current approaches suffer from several drawbacks, such as the inability to capture the deep semantics of language and sensitivity to minor input variations, resulting in significant changes in the generated text. In this paper, we present a novel solution to these challenges by employing a mixture of experts, multiple encoders, to offer distinct perspectives on the emotional state of the user{'}s utterance while simultaneously enhancing performance. We propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots, enabling the generation of empathetic responses that are fluent and relevant. In contrast to traditional attention mechanisms, the proposed model employs a specialized attention strategy that uniquely zeroes in on sentiment and emotion nuances within the user{'}s utterance. This ensures the generation of context-rich representations tailored to the underlying emotional tone and sentiment intricacies of the text. Our approach outperforms existing methods for generating empathetic embeddings, providing empathetic and diverse responses. The performance of our proposed model significantly exceeds that of existing models, enhancing emotion detection accuracy by 6.2{\%} and lexical diversity by 1.4{\%}. ASEM code is released at https://github.com/MIRAH-Official/Empathetic-Chatbot-ASEM.git

ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling

lrec-main.140

Poster

2402.16194

https://aclanthology.org/2024.lrec-main.141.bib

https://aclanthology.org/2024.lrec-main.141/

@inproceedings{kim-etal-2024-single, title = "A Single Linear Layer Yields Task-Adapted Low-Rank Matrices", author = "Kim, Hwichan and Sasaki, Shota and Hoshino, Sho and Honda, Ukyo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.141", pages = "1602--1608", abstract = "Low-Rank Adaptation (LoRA) is a widely used Parameter-Efficient Fine-Tuning (PEFT) method that updates an initial weight matrix $W_0$ with a delta matrix $\Delta W$ consisted by two low-rank matrices $A$ and $B$. A previous study suggested that there is correlation between $W_0$ and $\Delta W$. In this study, we aim to delve deeper into relationships between $W_0$ and low-rank matrices $A$ and $B$ to further comprehend the behavior of LoRA. In particular, we analyze a conversion matrix that transform $W_0$ into low-rank matrices, which encapsulates information about the relationships. Our analysis reveals that the conversion matrices are similar across each layer. Inspired by these findings, we hypothesize that a single linear layer, which takes each layer{'}s $W_0$ as input, can yield task-adapted low-rank matrices. To confirm this hypothesis, we devise a method named Conditionally Parameterized LoRA (CondLoRA) that updates initial weight matrices with low-rank matrices derived from a single linear layer. Our empirical results show that CondLoRA maintains a performance on par with LoRA, despite the fact that the trainable parameters of CondLoRA are fewer than those of LoRA. Therefore, we conclude that {``}a single linear layer yields task-adapted low-rank matrices.{''} The code used in our experiments is available at \url{https://github.com/CyberAgentAILab/CondLoRA}.", }

Low-Rank Adaptation (LoRA) is a widely used Parameter-Efficient Fine-Tuning (PEFT) method that updates an initial weight matrix $W_0$ with a delta matrix $\Delta W$ consisted by two low-rank matrices $A$ and $B$. A previous study suggested that there is correlation between $W_0$ and $\Delta W$. In this study, we aim to delve deeper into relationships between $W_0$ and low-rank matrices $A$ and $B$ to further comprehend the behavior of LoRA. In particular, we analyze a conversion matrix that transform $W_0$ into low-rank matrices, which encapsulates information about the relationships. Our analysis reveals that the conversion matrices are similar across each layer. Inspired by these findings, we hypothesize that a single linear layer, which takes each layer{'}s $W_0$ as input, can yield task-adapted low-rank matrices. To confirm this hypothesis, we devise a method named Conditionally Parameterized LoRA (CondLoRA) that updates initial weight matrices with low-rank matrices derived from a single linear layer. Our empirical results show that CondLoRA maintains a performance on par with LoRA, despite the fact that the trainable parameters of CondLoRA are fewer than those of LoRA. Therefore, we conclude that {``}a single linear layer yields task-adapted low-rank matrices.{''} The code used in our experiments is available at \url{https://github.com/CyberAgentAILab/CondLoRA}.

A Single Linear Layer Yields Task-Adapted Low-Rank Matrices

lrec-main.141

Poster

2403.14946

https://aclanthology.org/2024.lrec-main.142.bib

https://aclanthology.org/2024.lrec-main.142/

@inproceedings{uddin-etal-2024-asking, title = "Asking and Answering Questions to Extract Event-Argument Structures", author = "Uddin, Md Nayem and George, Enfa Rose and Blanco, Eduardo and Corman, Steven R.", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.142", pages = "1609--1626", abstract = "This paper presents a question-answering approach to extract document-level event-argument structures. We automatically ask and answer questions for each argument type an event may have. Questions are generated using manually defined templates and generative transformers. Template-based questions are generated using predefined role-specific wh-words and event triggers from the context document. Transformer-based questions are generated using large language models trained to formulate questions based on a passage and the expected answer. Additionally, we develop novel data augmentation strategies specialized in inter-sentential event-argument relations. We use a simple span-swapping technique, coreference resolution, and large language models to augment the training instances. Our approach enables transfer learning without any corpora-specific modifications and yields competitive results with the RAMS dataset. It outperforms previous work, and it is especially beneficial to extract arguments that appear in different sentences than the event trigger. We also present detailed quantitative and qualitative analyses shedding light on the most common errors made by our best model.", }

This paper presents a question-answering approach to extract document-level event-argument structures. We automatically ask and answer questions for each argument type an event may have. Questions are generated using manually defined templates and generative transformers. Template-based questions are generated using predefined role-specific wh-words and event triggers from the context document. Transformer-based questions are generated using large language models trained to formulate questions based on a passage and the expected answer. Additionally, we develop novel data augmentation strategies specialized in inter-sentential event-argument relations. We use a simple span-swapping technique, coreference resolution, and large language models to augment the training instances. Our approach enables transfer learning without any corpora-specific modifications and yields competitive results with the RAMS dataset. It outperforms previous work, and it is especially beneficial to extract arguments that appear in different sentences than the event trigger. We also present detailed quantitative and qualitative analyses shedding light on the most common errors made by our best model.

Asking and Answering Questions to Extract Event-Argument Structures

lrec-main.142

Poster

2404.16413

https://aclanthology.org/2024.lrec-main.143.bib

https://aclanthology.org/2024.lrec-main.143/

@inproceedings{baruah-etal-2024-assamesebacktranslit, title = "{A}ssamese{B}ack{T}ranslit: Back Transliteration of {R}omanized {A}ssamese Social Media Text", author = "Baruah, Hemanta and Singh, Sanasam Ranbir and Sarmah, Priyankoo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.143", pages = "1627--1637", abstract = "This paper presents a novel back transliteration dataset capturing native language text originally composed in the Roman/Latin script, harvested from popular social media platforms, along with its corresponding representation in the native Assamese script. Assamese, categorized as a low-resource language within the Indo-Aryan language family, predominantly spoken in the north-east Indian state of Assam, faces a scarcity of linguistic resources. The dataset comprises a total of 60,312 Roman-native parallel transliterated sentences. This paper diverges from conventional forward transliteration datasets consisting mainly of named entities and technical terms, instead presenting a novel transliteration dataset cultivated from three prominent social media platforms, Facebook, Twitter(currently X), and YouTube, in the backward transliteration direction. The paper offers a comprehensive examination of ten state-of-the-art word-level transliteration models within the context of this dataset, encompassing transliteration evaluation benchmarks, extensive performance assessments, and a discussion of the unique chal- lenges encountered during the processing of transliterated social media content. Our approach involves the initial use of two statistical transliteration models, followed by the training of two state-of-the-art neural network-based transliteration models, evaluation of three publicly available pre-trained models, and ultimately fine-tuning one existing state-of-the-art multilingual transliteration model along with two pre-trained large language models using the collected datasets. Notably, the Neural Transformer model outperforms all other baseline transliteration models, achieving the lowest Word Error Rate (WER) and Character Error Rate (CER), and the highest BLEU (up to 4 gram) score of 55.05, 19.44, and 69.15, respectively.", }

This paper presents a novel back transliteration dataset capturing native language text originally composed in the Roman/Latin script, harvested from popular social media platforms, along with its corresponding representation in the native Assamese script. Assamese, categorized as a low-resource language within the Indo-Aryan language family, predominantly spoken in the north-east Indian state of Assam, faces a scarcity of linguistic resources. The dataset comprises a total of 60,312 Roman-native parallel transliterated sentences. This paper diverges from conventional forward transliteration datasets consisting mainly of named entities and technical terms, instead presenting a novel transliteration dataset cultivated from three prominent social media platforms, Facebook, Twitter(currently X), and YouTube, in the backward transliteration direction. The paper offers a comprehensive examination of ten state-of-the-art word-level transliteration models within the context of this dataset, encompassing transliteration evaluation benchmarks, extensive performance assessments, and a discussion of the unique chal- lenges encountered during the processing of transliterated social media content. Our approach involves the initial use of two statistical transliteration models, followed by the training of two state-of-the-art neural network-based transliteration models, evaluation of three publicly available pre-trained models, and ultimately fine-tuning one existing state-of-the-art multilingual transliteration model along with two pre-trained large language models using the collected datasets. Notably, the Neural Transformer model outperforms all other baseline transliteration models, achieving the lowest Word Error Rate (WER) and Character Error Rate (CER), and the highest BLEU (up to 4 gram) score of 55.05, 19.44, and 69.15, respectively.

AssameseBackTranslit: Back Transliteration of Romanized Assamese Social Media Text

lrec-main.143

Poster

https://aclanthology.org/2024.lrec-main.144.bib

https://aclanthology.org/2024.lrec-main.144/

@inproceedings{behzad-etal-2024-assessing, title = "Assessing Online Writing Feedback Resources: Generative {AI} vs. Good Samaritans", author = "Behzad, Shabnam and Kashefi, Omid and Somasundaran, Swapna", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.144", pages = "1638--1644", abstract = "Providing constructive feedback on student essays is a critical factor in improving educational results; however, it presents notable difficulties and may demand substantial time investments, especially when aiming to deliver individualized and informative guidance. This study undertakes a comparative analysis of two readily available online resources for students seeking to hone their skills in essay writing for English proficiency tests: 1) essayforum.com, a widely used platform where students can submit their essays and receive feedback from volunteer educators at no cost, and 2) Large Language Models (LLMs) such as ChatGPT. By contrasting the feedback obtained from these two resources, we posit that they can mutually reinforce each other and are more helpful if employed in conjunction when seeking no-cost online assistance. The findings of this research shed light on the challenges of providing personalized feedback and highlight the potential of AI in advancing the field of automated essay evaluation.", }

Providing constructive feedback on student essays is a critical factor in improving educational results; however, it presents notable difficulties and may demand substantial time investments, especially when aiming to deliver individualized and informative guidance. This study undertakes a comparative analysis of two readily available online resources for students seeking to hone their skills in essay writing for English proficiency tests: 1) essayforum.com, a widely used platform where students can submit their essays and receive feedback from volunteer educators at no cost, and 2) Large Language Models (LLMs) such as ChatGPT. By contrasting the feedback obtained from these two resources, we posit that they can mutually reinforce each other and are more helpful if employed in conjunction when seeking no-cost online assistance. The findings of this research shed light on the challenges of providing personalized feedback and highlight the potential of AI in advancing the field of automated essay evaluation.

Assessing Online Writing Feedback Resources: Generative AI vs. Good Samaritans

lrec-main.144

Poster

https://aclanthology.org/2024.lrec-main.145.bib

https://aclanthology.org/2024.lrec-main.145/

@inproceedings{gan-etal-2024-assessing, title = "Assessing the Capabilities of Large Language Models in Coreference: An Evaluation", author = "Gan, Yujian and Poesio, Massimo and Yu, Juntao", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.145", pages = "1645--1665", abstract = "This paper offers a nuanced examination of the role Large Language Models (LLMs) play in coreference resolution, aimed at guiding the future direction in the era of LLMs. We carried out both manual and automatic analyses of different LLMs{'} abilities, employing different prompts to examine the performance of different LLMs, obtaining a comprehensive view of their strengths and weaknesses. We found that LLMs show exceptional ability in understanding coreference. However, harnessing this ability to achieve state of the art results on traditional datasets and benchmarks isn{'}t straightforward. Given these findings, we propose that future efforts should: (1) Improve the scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. (2) Enhance the fine-grained language understanding capabilities of LLMs.", }

This paper offers a nuanced examination of the role Large Language Models (LLMs) play in coreference resolution, aimed at guiding the future direction in the era of LLMs. We carried out both manual and automatic analyses of different LLMs{'} abilities, employing different prompts to examine the performance of different LLMs, obtaining a comprehensive view of their strengths and weaknesses. We found that LLMs show exceptional ability in understanding coreference. However, harnessing this ability to achieve state of the art results on traditional datasets and benchmarks isn{'}t straightforward. Given these findings, we propose that future efforts should: (1) Improve the scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. (2) Enhance the fine-grained language understanding capabilities of LLMs.

Assessing the Capabilities of Large Language Models in Coreference: An Evaluation

lrec-main.145

Poster

https://aclanthology.org/2024.lrec-main.146.bib

https://aclanthology.org/2024.lrec-main.146/

@inproceedings{wang-yuan-2024-assessing, title = "Assessing the Efficacy of Grammar Error Correction: A Human Evaluation Approach in the {J}apanese Context", author = "Wang, Qiao and Yuan, Zheng", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.146", pages = "1666--1672", abstract = "In this study, we evaluated the performance of the state-of-the-art sequence tagging grammar error detection and correction model (SeqTagger) using Japanese university students{'} writing samples. With an automatic annotation toolkit, ERRANT, we first evaluated SeqTagger{'}s performance on error correction with human expert correction as the benchmark. Then a human-annotated approach was adopted to evaluate Seqtagger{'}s performance in error detection using a subset of the writing dataset. Results indicated a precision of 63.66{\%} and a recall of 20.19{\%} for error correction in the full dataset. For the subset, after manual exclusion of irrelevant errors such as semantic and mechanical ones, the model shows an adjusted precision of 97.98{\%} and an adjusted recall of 42.98{\%} for error detection, indicating the model{'}s high accuracy but also its conservativeness. Thematic analysis on errors undetected by the model revealed that determiners and articles, especially the latter, were predominant. Specifically, in terms of context-independent errors, the model occasionally overlooked basic ones and faced challenges with overly erroneous or complex structures. Meanwhile, context-dependent errors, notably those related to tense and noun number, as well as those possibly influenced by the students{'} first language (L1), remained particularly challenging.", }

In this study, we evaluated the performance of the state-of-the-art sequence tagging grammar error detection and correction model (SeqTagger) using Japanese university students{'} writing samples. With an automatic annotation toolkit, ERRANT, we first evaluated SeqTagger{'}s performance on error correction with human expert correction as the benchmark. Then a human-annotated approach was adopted to evaluate Seqtagger{'}s performance in error detection using a subset of the writing dataset. Results indicated a precision of 63.66{\%} and a recall of 20.19{\%} for error correction in the full dataset. For the subset, after manual exclusion of irrelevant errors such as semantic and mechanical ones, the model shows an adjusted precision of 97.98{\%} and an adjusted recall of 42.98{\%} for error detection, indicating the model{'}s high accuracy but also its conservativeness. Thematic analysis on errors undetected by the model revealed that determiners and articles, especially the latter, were predominant. Specifically, in terms of context-independent errors, the model occasionally overlooked basic ones and faced challenges with overly erroneous or complex structures. Meanwhile, context-dependent errors, notably those related to tense and noun number, as well as those possibly influenced by the students{'} first language (L1), remained particularly challenging.

Assessing the Efficacy of Grammar Error Correction: A Human Evaluation Approach in the Japanese Context

lrec-main.146

Poster

2402.18101

https://aclanthology.org/2024.lrec-main.147.bib

https://aclanthology.org/2024.lrec-main.147/

@inproceedings{xu-etal-2024-streamlined, title = "A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition", author = "Xu, Wenjie and Chen, Yidan and Ouyang, Jianquan", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.147", pages = "1673--1683", abstract = "Few-shot named entity recognition (NER) is a challenging task that aims to recognize new named entities with only a limited amount of labeled examples. In this paper, we introduce SSF, which is a streamlined span-based factorization method that addresses the problem of few-shot NER. Our approach formulates few-shot NER as a span-level alignment problem between query and support instances. To achieve this goal, SSF decomposes the span-level alignment problem into several refined span-level procedures. The proposed approach encompasses several key modules such as the Span Boosting Module, Span Prototypical Module, Span Alignment Module, and Span Optimization Module. Our experimental results demonstrate a significant improvement over the previous state-of-the-art performance. Specifically, compared to previous methods, our proposed approach achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on the SNIPS dataset. Moreover, our approach has surpassed the latest state-of-the-art performance on both datasets.", }

Few-shot named entity recognition (NER) is a challenging task that aims to recognize new named entities with only a limited amount of labeled examples. In this paper, we introduce SSF, which is a streamlined span-based factorization method that addresses the problem of few-shot NER. Our approach formulates few-shot NER as a span-level alignment problem between query and support instances. To achieve this goal, SSF decomposes the span-level alignment problem into several refined span-level procedures. The proposed approach encompasses several key modules such as the Span Boosting Module, Span Prototypical Module, Span Alignment Module, and Span Optimization Module. Our experimental results demonstrate a significant improvement over the previous state-of-the-art performance. Specifically, compared to previous methods, our proposed approach achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on the SNIPS dataset. Moreover, our approach has surpassed the latest state-of-the-art performance on both datasets.

A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition

lrec-main.147

Poster

https://aclanthology.org/2024.lrec-main.148.bib

https://aclanthology.org/2024.lrec-main.148/

@inproceedings{jang-etal-2024-study, title = "A Study on How Attention Scores in the {BERT} Model Are Aware of Lexical Categories in Syntactic and Semantic Tasks on the {GLUE} Benchmark", author = "Jang, Dongjun and Byun, Sungjoo and Shin, Hyopil", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.148", pages = "1684--1689", abstract = "This study examines whether the attention scores between tokens in the BERT model significantly vary based on lexical categories during the fine-tuning process for downstream tasks. Drawing inspiration from the notion that in human language processing, syntactic and semantic information is parsed differently, we categorize tokens in sentences according to their lexical categories and focus on changes in attention scores among these categories. Our hypothesis posits that in downstream tasks that prioritize semantic information, attention scores centered on content words are enhanced, while in cases emphasizing syntactic information, attention scores centered on function words are intensified. Through experimentation conducted on six tasks from the GLUE benchmark dataset, we substantiate our hypothesis regarding the fine-tuning process. Furthermore, our additional investigations reveal the presence of BERT layers that consistently assign more bias to specific lexical categories, irrespective of the task, highlighting the existence of task-agnostic lexical category preferences.", }

This study examines whether the attention scores between tokens in the BERT model significantly vary based on lexical categories during the fine-tuning process for downstream tasks. Drawing inspiration from the notion that in human language processing, syntactic and semantic information is parsed differently, we categorize tokens in sentences according to their lexical categories and focus on changes in attention scores among these categories. Our hypothesis posits that in downstream tasks that prioritize semantic information, attention scores centered on content words are enhanced, while in cases emphasizing syntactic information, attention scores centered on function words are intensified. Through experimentation conducted on six tasks from the GLUE benchmark dataset, we substantiate our hypothesis regarding the fine-tuning process. Furthermore, our additional investigations reveal the presence of BERT layers that consistently assign more bias to specific lexical categories, irrespective of the task, highlighting the existence of task-agnostic lexical category preferences.

A Study on How Attention Scores in the BERT Model Are Aware of Lexical Categories in Syntactic and Semantic Tasks on the GLUE Benchmark

lrec-main.148

Poster

2403.16447

https://aclanthology.org/2024.lrec-main.149.bib

https://aclanthology.org/2024.lrec-main.149/

@inproceedings{zhu-etal-2024-survey, title = "A Survey on Natural Language Processing for Programming", author = "Zhu, Qingfu and Luo, Xianzhen and Liu, Fang and Gao, Cuiyun and Che, Wanxiang", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.149", pages = "1690--1704", abstract = "Natural language processing for programming aims to use NLP techniques to assist programming. It is increasingly prevalent for its effectiveness in improving productivity. Distinct from natural language, a programming language is highly structured and functional. Constructing a structure-based representation and a functionality-oriented algorithm is at the heart of program understanding and generation. In this paper, we conduct a systematic review covering tasks, datasets, evaluation methods, techniques, and models from the perspective of the structure-based and functionality-oriented property, aiming to understand the role of the two properties in each component. Based on the analysis, we illustrate unexplored areas and suggest potential directions for future work.", }

Natural language processing for programming aims to use NLP techniques to assist programming. It is increasingly prevalent for its effectiveness in improving productivity. Distinct from natural language, a programming language is highly structured and functional. Constructing a structure-based representation and a functionality-oriented algorithm is at the heart of program understanding and generation. In this paper, we conduct a systematic review covering tasks, datasets, evaluation methods, techniques, and models from the perspective of the structure-based and functionality-oriented property, aiming to understand the role of the two properties in each component. Based on the analysis, we illustrate unexplored areas and suggest potential directions for future work.

A Survey on Natural Language Processing for Programming

lrec-main.149

Poster

2212.05773

https://aclanthology.org/2024.lrec-main.150.bib

https://aclanthology.org/2024.lrec-main.150/

@inproceedings{barros-etal-2024-tool, title = "A Tool for Determining Distances and Overlaps between Multimodal Annotations", author = "Barros, Camila Antonio and Cipri{\'a}n-S{\'a}nchez, Jorge Francisco and Santos, Saulo Mendes", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.150", pages = "1705--1714", abstract = "Comparing annotations is a constant and necessary step in corpus analysis. Although the nature of these annotations is normally research-specific, the tools used for this purpose do not have to be. Here, we present a tool for extracting and comparing annotations from ELAN, despite their idiosyncrasies. The intention behind this tool is to provide a handy way to analyze ELAN annotated files, by comparing tiers to a reference unit. Using the presented tool, it is possible to see how tiers overlap (even if they are of symbolic type), to which ratio, and the displacement regarding a reference unit. We present an example of multimodal corpus analysis, regarding the coordination between speech and gesture units based on a pragmatic reference. We argue that looking into overlap ratios can be more informative of the association between speech and gestures, and that considering a time buffer between speech and gestural events can be misleading.", }

Comparing annotations is a constant and necessary step in corpus analysis. Although the nature of these annotations is normally research-specific, the tools used for this purpose do not have to be. Here, we present a tool for extracting and comparing annotations from ELAN, despite their idiosyncrasies. The intention behind this tool is to provide a handy way to analyze ELAN annotated files, by comparing tiers to a reference unit. Using the presented tool, it is possible to see how tiers overlap (even if they are of symbolic type), to which ratio, and the displacement regarding a reference unit. We present an example of multimodal corpus analysis, regarding the coordination between speech and gesture units based on a pragmatic reference. We argue that looking into overlap ratios can be more informative of the association between speech and gestures, and that considering a time buffer between speech and gestural events can be misleading.

A Tool for Determining Distances and Overlaps between Multimodal Annotations

lrec-main.150

Poster

https://aclanthology.org/2024.lrec-main.151.bib

https://aclanthology.org/2024.lrec-main.151/

@inproceedings{vligouridou-etal-2024-treebank, title = "A Treebank of {A}sia Minor {G}reek", author = {Vligouridou, Eleni and Iliadou, Inessa and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.151", pages = "1715--1721", abstract = "Asia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and culture that face a dire struggle for preservation due to declining speaker base and scarce linguistic resources. To address this need, we introduce a Universal Dependencies treebank of Pharasiot Greek, one of the severly endangerd AMG dialects. The present treebank is fully manually annotated and currently consists of 350 sentences from six fairy tales in Pharasiot dialect. Besides describing the treebank and the annotation process, we provide and discuss interesting phenomena we observed in the treebank. Most phenomena we discuss are related to contact-induced linguistic changes that these dialects are well known for. Beyond linguistic inquiry, like other treebanks for truly low-resource languages, the AMG treebank we present offers potentials for diverse applications, such as language preservation and revitalization, as well as NLP tools that have to be developed with scarce resources.", }

Asia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and cultAsia Minor Greek (AMG) dialects are endangered dialects rich in history and culture that face a dire struggle for preservation due to declining speaker base and scarce linguistic resources. To address this need, we introduce a Universal Dependencies treebank of Pharasiot Greek, one of the severly endangerd AMG dialects. The present treebank is fully manually annotated and currently consists of 350 sentences from six fairy tales in Pharasiot dialect. Besides describing the treebank and the annotation process, we provide and discuss interesting phenomena we observed in the treebank. Most phenomena we discuss are related to contact-induced linguistic changes that these dialects are well known for. Beyond linguistic inquiry, like other treebanks for truly low-resource languages, the AMG treebank we present offers potentials for diverse applications, such as language preservation and revitalization, as well as NLP tools that have to be developed with scarce resources.

A Treebank of Asia Minor Greek

lrec-main.151

Poster

https://aclanthology.org/2024.lrec-main.152.bib

https://aclanthology.org/2024.lrec-main.152/

@inproceedings{yang-2024-trusted, title = "A Trusted Multi-View Evidential Fusion Framework for Commonsense Reasoning", author = "Yang, Shuo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.152", pages = "1722--1733", abstract = "While deep learning models are powerful, they have limitations in tasks that require commonsense reasoning, as these tasks often involve interpreting information that may not be directly available in the input. Providing evidence has been proven to significantly enhance performance in commonsense reasoning tasks. However, there are various perspectives on evidence, including natural language explanations generated by pre-trained language models, facts derived from world knowledge like text corpora and knowledge bases, and rationales extracted from the input context. Hence, it is crucial to determine how to estimate the confidence degree of different evidence and how to combine them reliably. To address these challenges, this study proposes a trusted multi-view evidential fusion framework for reliable commonsense reasoning tasks that dynamically assesses the confidence of evidence and combines different views of evidence in a trustworthy manner. The proposed method is applied to three commonsense question-answering benchmarks, demonstrating that this approach can effectively reason with multi-view evidence and can compete with state-of-the-art performance.", }

While deep learning models are powerful, they have limitations in tasks that require commonsense reasoning, as these tasks often involve interpreting information that may not be directly available in the input. Providing evidence has been proven to significantly enhance performance in commonsense reasoning tasks. However, there are various perspectives on evidence, including natural language explanations generated by pre-trained language models, facts derived from world knowledge like text corpora and knowledge bases, and rationales extracted from the input context. Hence, it is crucial to determine how to estimate the confidence degree of different evidence and how to combine them reliably. To address these challenges, this study proposes a trusted multi-view evidential fusion framework for reliable commonsense reasoning tasks that dynamically assesses the confidence of evidence and combines different views of evidence in a trustworthy manner. The proposed method is applied to three commonsense question-answering benchmarks, demonstrating that this approach can effectively reason with multi-view evidence and can compete with state-of-the-art performance.

A Trusted Multi-View Evidential Fusion Framework for Commonsense Reasoning

lrec-main.152

Poster

https://aclanthology.org/2024.lrec-main.153.bib

https://aclanthology.org/2024.lrec-main.153/

@inproceedings{yang-etal-2024-attack, title = "Attack Named Entity Recognition by Entity Boundary Interference", author = "Yang, Yifei and Wu, Hongqiu and Zhao, Hai", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.153", pages = "1734--1744", abstract = "Named Entity Recognition (NER) is a cornerstone natural language processing task while its robustness has been given little attention. This paper rethinks the principles of the conventional text attack, as they can easily violate the label consistency between the original and adversarial NER samples. This is due to the fine-grained nature of NER, as even minor word changes in the sentence can result in the emergence or mutation of any entity, producing invalid adversarial samples. To this end, we propose a novel one-word modification NER attack based on a key insight, NER models are always vulnerable to the boundary position of an entity to make their decision. We thus strategically insert a new boundary into the sentence and trigger the victim model to make a wrong recognition either on this boundary word or on other words in the sentence. We call this attack \textit{Virtual Boundary Attack (ViBA)}, which is shown to be remarkably effective when attacking both English and Chinese models with a 70{\%}-90{\%} attack success rate on state-of-the-art language models, and also significantly faster than previous methods.", }

Named Entity Recognition (NER) is a cornerstone natural language processing task while its robustness has been given little attention. This paper rethinks the principles of the conventional text attack, as they can easily violate the label consistency between the original and adversarial NER samples. This is due to the fine-grained nature of NER, as even minor word changes in the sentence can result in the emergence or mutation of any entity, producing invalid adversarial samples. To this end, we propose a novel one-word modification NER attack based on a key insight, NER models are always vulnerable to the boundary position of an entity to make their decision. We thus strategically insert a new boundary into the sentence and trigger the victim model to make a wrong recognition either on this boundary word or on other words in the sentence. We call this attack \textit{Virtual Boundary Attack (ViBA)}, which is shown to be remarkably effective when attacking both English and Chinese models with a 70{\%}-90{\%} attack success rate on state-of-the-art language models, and also significantly faster than previous methods.

Attack Named Entity Recognition by Entity Boundary Interference

lrec-main.153

Poster

2305.05253

https://aclanthology.org/2024.lrec-main.154.bib

https://aclanthology.org/2024.lrec-main.154/

@inproceedings{smidt-etal-2024-crossroad, title = "At the Crossroad of Cuneiform and {NLP}: Challenges for Fine-grained Part-of-speech Tagging", author = "Smidt, Gustav Ryberg and Lefever, Els and de Graef, Katrien", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.154", pages = "1745--1755", abstract = "The study of ancient Middle Eastern cultures is dominated by the vast number of cuneiform texts. Multiple languages and language families were expressed in cuneiform. The most dominant language written in cuneiform is the Semitic Akkadian, which is the focus of this paper. We are specifically focusing on letters written in the dialect used in modern-day Baghdad and south towards the Persian Gulf during the Old Babylonian period (c. 2000-1600 B.C.E.). The Akkadian language was rediscovered in the 19th century and is now being scrutinised by Natural Language Processing (NLP) methods. However, existing Akkadian text publications are not always suitable for digital editions. We therefore risk applying NLP methods onto renderings of Akkadian unfit for the purpose. In this paper we want to investigate the input material and try to initiate a discussion about best-practices in the crossroad where NLP meets cuneiform studies. Specifically, we want to question the use of pre-trained embeddings, sentence segmentation and the type of cuneiform input used to fine-tune language models for the task of fine-grained part-of-speech tagging. We examine the issues by theoretical and practical approaches in a way that we hope spurs discussions that are relevant for automatic processing of other ancient languages.", }

The study of ancient Middle Eastern cultures is dominated by the vast number of cuneiform texts. Multiple languages and language families were expressed in cuneiform. The most dominant language written in cuneiform is the Semitic Akkadian, which is the focus of this paper. We are specifically focusing on letters written in the dialect used in modern-day Baghdad and south towards the Persian Gulf during the Old Babylonian period (c. 2000-1600 B.C.E.). The Akkadian language was rediscovered in the 19th century and is now being scrutinised by Natural Language Processing (NLP) methods. However, existing Akkadian text publications are not always suitable for digital editions. We therefore risk applying NLP methods onto renderings of Akkadian unfit for the purpose. In this paper we want to investigate the input material and try to initiate a discussion about best-practices in the crossroad where NLP meets cuneiform studies. Specifically, we want to question the use of pre-trained embeddings, sentence segmentation and the type of cuneiform input used to fine-tune language models for the task of fine-grained part-of-speech tagging. We examine the issues by theoretical and practical approaches in a way that we hope spurs discussions that are relevant for automatic processing of other ancient languages.

At the Crossroad of Cuneiform and NLP: Challenges for Fine-grained Part-of-speech Tagging

lrec-main.154

Poster

https://aclanthology.org/2024.lrec-main.155.bib

https://aclanthology.org/2024.lrec-main.155/

@inproceedings{narayanan-aepli-2024-tulu, title = "A {T}ulu Resource for Machine Translation", author = {Narayanan, Manu and Aepli, No{\"e}mi}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.155", pages = "1756--1767", abstract = "We present the first parallel dataset for English{--}Tulu translation. Tulu, classified within the South Dravidian linguistic family branch, is predominantly spoken by approximately 2.5 million individuals in southwestern India. Our dataset is constructed by integrating human translations into the multilingual machine translation resource FLORES-200. Furthermore, we use this dataset for evaluation purposes in developing our English{--}Tulu machine translation model. For the model{'}s training, we leverage resources available for related South Dravidian languages. We adopt a transfer learning approach that exploits similarities between high-resource and low-resource languages. This method enables the training of a machine translation system even in the absence of parallel data between the source and target language, thereby overcoming a significant obstacle in machine translation development for low-resource languages. Our English{--}Tulu system, trained without using parallel English{--}Tulu data, outperforms Google Translate by 19 BLEU points (in September 2023). The dataset and code are available here: https://github.com/manunarayanan/Tulu-NMT.", }

We present the first parallel dataset for English{--}Tulu translation. Tulu, classified within the South Dravidian linguistic family branch, is predominantly spoken by approximately 2.5 million individuals in southwestern India. Our dataset is constructed by integrating human translations into the multilingual machine translation resource FLORES-200. Furthermore, we use this dataset for evaluation purposes in developing our English{--}Tulu machine translation model. For the model{'}s training, we leverage resources available for related South Dravidian languages. We adopt a transfer learning approach that exploits similarities between high-resource and low-resource languages. This method enables the training of a machine translation system even in the absence of parallel data between the source and target language, thereby overcoming a significant obstacle in machine translation development for low-resource languages. Our English{--}Tulu system, trained without using parallel English{--}Tulu data, outperforms Google Translate by 19 BLEU points (in September 2023). The dataset and code are available here: https://github.com/manunarayanan/Tulu-NMT.

A Tulu Resource for Machine Translation

lrec-main.155

Poster

2403.19142

https://aclanthology.org/2024.lrec-main.156.bib

https://aclanthology.org/2024.lrec-main.156/

@inproceedings{feng-etal-2024-two, title = "A Two-Stage Framework with Self-Supervised Distillation for Cross-Domain Text Classification", author = "Feng, Yunlong and Li, Bohan and Qin, Libo and Xu, Xiao and Che, Wanxiang", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.156", pages = "1768--1777", abstract = "Cross-domain text classification is a crucial task as it enables models to adapt to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source domain(s) and unlabeled data from the target domain. To this end, previous work focuses on either extracting domain-invariant features or task-agnostic features, ignoring domain-aware features that may be present in the target domain and could be useful for the downstream task. In this paper, we propose a two-stage framework for cross-domain text classification. In the first stage, we finetune the model with mask language modeling (MLM) and labeled data from the source domain. In the second stage, we further fine-tune the model with \textit{self-supervised distillation} (SSD) and unlabeled data from the target domain. We evaluate its performance on a public cross-domain text classification benchmark and the experiment results show that our method achieves new state-of-the-art results for both single-source domain adaptations (94.17{\%} +1.03{\%}) and multi-source domain adaptations (95.09{\%} +1.34{\%}).", }

Cross-domain text classification is a crucial task as it enables models to adapt to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source domain(s) and unlabeled data from the target domain. To this end, previous work focuses on either extracting domain-invariant features or task-agnostic features, ignoring domain-aware features that may be present in the target domain and could be useful for the downstream task. In this paper, we propose a two-stage framework for cross-domain text classification. In the first stage, we finetune the model with mask language modeling (MLM) and labeled data from the source domain. In the second stage, we further fine-tune the model with \textit{self-supervised distillation} (SSD) and unlabeled data from the target domain. We evaluate its performance on a public cross-domain text classification benchmark and the experiment results show that our method achieves new state-of-the-art results for both single-source domain adaptations (94.17{\%} +1.03{\%}) and multi-source domain adaptations (95.09{\%} +1.34{\%}).

A Two-Stage Framework with Self-Supervised Distillation for Cross-Domain Text Classification

lrec-main.156

Poster

2304.09820

https://aclanthology.org/2024.lrec-main.157.bib

https://aclanthology.org/2024.lrec-main.157/

@inproceedings{chen-etal-2024-two, title = "A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent {NLU}", author = "Chen, Guanhua and Yao, Yutong and Wong, Derek F. and Chao, Lidia S.", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.157", pages = "1778--1788", abstract = "Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance. While previous works train the model contrastively to increase the margin between different multi-intent labels, they are less suited to the nuances of multi-intent NLU. They ignore the rich information between the shared intents, which is beneficial to constructing a better embedding space, especially in low-data scenarios. We introduce a two-stage Prediction-Aware Contrastive Learning (PACL) framework for multi-intent NLU to harness this valuable knowledge. Our approach capitalizes on shared intent information by integrating word-level pre-training and prediction-aware contrastive fine-tuning. We construct a pre-training dataset using a word-level data augmentation strategy. Subsequently, our framework dynamically assigns roles to instances during contrastive fine-tuning while introducing a prediction-aware contrastive loss to maximize the impact of contrastive learning. We present experimental results and empirical analysis conducted on three widely used datasets, demonstrating that our method surpasses the performance of three prominent baselines on both low-data and full-data scenarios.", }

Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance. While previous works train the model contrastively to increase the margin between different multi-intent labels, they are less suited to the nuances of multi-intent NLU. They ignore the rich information between the shared intents, which is beneficial to constructing a better embedding space, especially in low-data scenarios. We introduce a two-stage Prediction-Aware Contrastive Learning (PACL) framework for multi-intent NLU to harness this valuable knowledge. Our approach capitalizes on shared intent information by integrating word-level pre-training and prediction-aware contrastive fine-tuning. We construct a pre-training dataset using a word-level data augmentation strategy. Subsequently, our framework dynamically assigns roles to instances during contrastive fine-tuning while introducing a prediction-aware contrastive loss to maximize the impact of contrastive learning. We present experimental results and empirical analysis conducted on three widely used datasets, demonstrating that our method surpasses the performance of three prominent baselines on both low-data and full-data scenarios.

A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU

lrec-main.157

Poster

2405.02925

https://aclanthology.org/2024.lrec-main.158.bib

https://aclanthology.org/2024.lrec-main.158/

@inproceedings{singh-manandise-2024-typology, title = "A Typology of Errors for User Utterances in Chatbots", author = "Singh, Anu and Manandise, Esme", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.158", pages = "1789--1794", abstract = "This paper discusses the challenges non-prescriptive language uses in chatbot communication create for Semantic Parsing (SP). To help SP developers improve their systems, we propose a flexible error typology based on an analysis of a sample of non-prescriptive language uses mined from a domain-specific chatbot logs. This typology is not tied to any specific language model. We also present a framework for automatically mapping these errors to the typology. Finally, we show how our framework can help evaluate SP systems from a linguistic robustness perspective. Our framework can be expanded to include new classes of errors across different domains and user demographics.", }

This paper discusses the challenges non-prescriptive language uses in chatbot communication create for Semantic Parsing (SP). To help SP developers improve their systems, we propose a flexible error typology based on an analysis of a sample of non-prescriptive language uses mined from a domain-specific chatbot logs. This typology is not tied to any specific language model. We also present a framework for automatically mapping these errors to the typology. Finally, we show how our framework can help evaluate SP systems from a linguistic robustness perspective. Our framework can be expanded to include new classes of errors across different domains and user demographics.

A Typology of Errors for User Utterances in Chatbots

lrec-main.158

Poster

https://aclanthology.org/2024.lrec-main.159.bib

https://aclanthology.org/2024.lrec-main.159/

@inproceedings{felice-etal-2024-audiocite, title = "Audiocite.net : A Large Spoken Read Dataset in {F}rench", author = "Felice, Soline and Evain, Solene Virginie and Rossato, Solange and Portet, Fran{\c{c}}ois", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.159", pages = "1795--1800", abstract = "The advent of self-supervised learning (SSL) in speech processing has allowed the use of large unlabeled datasets to learn pre-trained models, serving as powerful encoders for various downstream tasks. However, the application of these SSL methods to languages such as French has proved difficult due to the scarcity of large French speech datasets. To advance the emergence of pre-trained models for French speech, we present the Audiocite.net corpus composed of 6,682 hours of recordings from 130 readers. This corpus is composed of audiobooks from the audiocite.net website, shared by 130 readers. In addition to describing the creation process and final statistics, we also show how this dataset impacted the models of LeBenchmark project in its 14k version for speech processing downstream tasks.", }

The advent of self-supervised learning (SSL) in speech processing has allowed the use of large unlabeled datasets to learn pre-trained models, serving as powerful encoders for various downstream tasks. However, the application of these SSL methods to languages such as French has proved difficult due to the scarcity of large French speech datasets. To advance the emergence of pre-trained models for French speech, we present the Audiocite.net corpus composed of 6,682 hours of recordings from 130 readers. This corpus is composed of audiobooks from the audiocite.net website, shared by 130 readers. In addition to describing the creation process and final statistics, we also show how this dataset impacted the models of LeBenchmark project in its 14k version for speech processing downstream tasks.

Audiocite.net : A Large Spoken Read Dataset in French

lrec-main.159

Poster

https://aclanthology.org/2024.lrec-main.160.bib

https://aclanthology.org/2024.lrec-main.160/

@inproceedings{zou-etal-2024-aurora, title = "{A}u{R}o{RA}: A One-for-all Platform for Augmented Reasoning and Refining with Task-Adaptive Chain-of-Thought Prompting", author = "Zou, Anni and Zhang, Zhuosheng and Zhao, Hai", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.160", pages = "1801--1807", abstract = "Large language models (LLMs) empowered by chain-of-thought (CoT) prompting have yielded remarkable prowess in reasoning tasks. Nevertheless, current methods predominantly lean on handcrafted or task-specific demonstrations, lack reliable knowledge basis and thus struggle for trustworthy responses in an automated pattern. While recent works endeavor to improve upon one certain aspect, they ignore the importance and necessity of establishing an integrated and interpretable reasoning system. To address these drawbacks and provide a universal solution, we propose AuRoRA: a one-for-all platform for augmented reasoning and refining based on CoT prompting that excels in adaptability, reliability, integrity, and interpretability. The system exhibits superior performances across six reasoning tasks and offers real-time visual analysis, which has pivotal academic and application value in the era of LLMs. The AuRoRA platform is available at https://huggingface.co/spaces/Anni123/AuRoRA.", }

Large language models (LLMs) empowered by chain-of-thought (CoT) prompting have yielded remarkable prowess in reasoning tasks. Nevertheless, current methods predominantly lean on handcrafted or task-specific demonstrations, lack reliable knowledge basis and thus struggle for trustworthy responses in an automated pattern. While recent works endeavor to improve upon one certain aspect, they ignore the importance and necessity of establishing an integrated and interpretable reasoning system. To address these drawbacks and provide a universal solution, we propose AuRoRA: a one-for-all platform for augmented reasoning and refining based on CoT prompting that excels in adaptability, reliability, integrity, and interpretability. The system exhibits superior performances across six reasoning tasks and offers real-time visual analysis, which has pivotal academic and application value in the era of LLMs. The AuRoRA platform is available at https://huggingface.co/spaces/Anni123/AuRoRA.

AuRoRA: A One-for-all Platform for Augmented Reasoning and Refining with Task-Adaptive Chain-of-Thought Prompting

lrec-main.160

Poster

https://aclanthology.org/2024.lrec-main.161.bib

https://aclanthology.org/2024.lrec-main.161/

@inproceedings{sevilla-etal-2024-automated, title = "Automated Extraction of Prosodic Structure from Unannotated Sign Language Video", author = "Sevilla, Antonio F. G. and Lahoz-Bengoechea, Jos{\'e} Mar{\'\i}a and Diaz, Alberto", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.161", pages = "1808--1816", abstract = "As in oral phonology, prosody is an important carrier of linguistic information in sign languages. One of the most prominent ways this reveals itself is in the time structure of signs: their rhythm and intensity of articulation. To be able to empirically see these effects, the velocity of the hands can be computed throughout the execution of a sign. In this article, we propose a method for extracting this information from unlabeled videos of sign language, exploiting CoTracker, a recent advancement in computer vision which can track every point in a video without the need of any calibration or fine-tuning. The dominant hand is identified via clustering of the computed point velocities, and its dynamic profile plotted to make apparent the prosodic structure of signing. We apply our method to different datasets and sign languages, and perform a preliminary visual exploration of results. This exploration supports the usefulness of our methodology for linguistic analysis, though issues to be tackled remain, such as bi-manual signs and a formal and numerical evaluation of accuracy. Nonetheless, the absence of any preprocessing requirements may make it useful for other researchers and datasets.", }

As in oral phonology, prosody is an important carrier of linguistic information in sign languages. One of the most prominent ways this reveals itself is in the time structure of signs: their rhythm and intensity of articulation. To be able to empirically see these effects, the velocity of the hands can be computed throughout the execution of a sign. In this article, we propose a method for extracting this information from unlabeled videos of sign language, exploiting CoTracker, a recent advancement in computer vision which can track every point in a video without the need of any calibration or fine-tuning. The dominant hand is identified via clustering of the computed point velocities, and its dynamic profile plotted to make apparent the prosodic structure of signing. We apply our method to different datasets and sign languages, and perform a preliminary visual exploration of results. This exploration supports the usefulness of our methodology for linguistic analysis, though issues to be tackled remain, such as bi-manual signs and a formal and numerical evaluation of accuracy. Nonetheless, the absence of any preprocessing requirements may make it useful for other researchers and datasets.

Automated Extraction of Prosodic Structure from Unannotated Sign Language Video

lrec-main.161

Poster

https://aclanthology.org/2024.lrec-main.162.bib

https://aclanthology.org/2024.lrec-main.162/

@inproceedings{gala-etal-2024-automatically, title = "Automatically Estimating Textual and Phonemic Complexity for Cued Speech: How to See the Sounds from {F}rench Texts", author = "Gala, N{\'u}ria and Bigi, Brigitte and Bauer, Marie", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.162", pages = "1817--1824", abstract = "In this position paper we present a methodology to automatically annotate French text for Cued Speech (CS), a communication system developed for people with hearing loss to complement speech reading at the phonetic level. This visual communication mode uses handshapes in different placements near the face in combination with the mouth movements (called {`}cues{'} or {`}keys{'}) to make the phonemes of spoken language look different from each other. CS is used to acquire skills in lip reading, in oral communication and for reading. Despite many studies demonstrating its benefits, there are few resources available for learning and practicing it, especially in French. We thus propose a methodology to phonemize written corpora so that each word is aligned with the corresponding CS key(s). This methodology is proposed as part of a wider project aimed at creating an augmented reality system displaying a virtual coding hand where the user will be able to choose a text upon its complexity for cueing.", }

In this position paper we present a methodology to automatically annotate French text for Cued Speech (CS), a communication system developed for people with hearing loss to complement speech reading at the phonetic level. This visual communication mode uses handshapes in different placements near the face in combination with the mouth movements (called {`}cues{'} or {`}keys{'}) to make the phonemes of spoken language look different from each other. CS is used to acquire skills in lip reading, in oral communication and for reading. Despite many studies demonstrating its benefits, there are few resources available for learning and practicing it, especially in French. We thus propose a methodology to phonemize written corpora so that each word is aligned with the corresponding CS key(s). This methodology is proposed as part of a wider project aimed at creating an augmented reality system displaying a virtual coding hand where the user will be able to choose a text upon its complexity for cueing.

Automatically Estimating Textual and Phonemic Complexity for Cued Speech: How to See the Sounds from French Texts

lrec-main.162

Poster

https://aclanthology.org/2024.lrec-main.163.bib

https://aclanthology.org/2024.lrec-main.163/

@inproceedings{tepei-bloem-2024-automatic, title = "Automatic {A}nimacy Classification for {R}omanian Nouns", author = "Tepei, Maria and Bloem, Jelke", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.163", pages = "1825--1831", abstract = "We introduce the first Romanian animacy classifier, specifically a type-based binary classifier of Romanian nouns into the classes human/non-human, using pre-trained word embeddings and animacy information derived from Romanian WordNet. By obtaining a seed set of labeled nouns and their embeddings, we are able to train classifiers that generalize to unseen nouns. We compare three different architectures and observe good performance on classifying word types. In addition, we manually annotate a small corpus for animacy to perform a token-based evaluation of Romanian animacy classification in a naturalistic setting, which reveals limitations of the type-based classification approach.", }

We introduce the first Romanian animacy classifier, specifically a type-based binary classifier of Romanian nouns into the classes human/non-human, using pre-trained word embeddings and animacy information derived from Romanian WordNet. By obtaining a seed set of labeled nouns and their embeddings, we are able to train classifiers that generalize to unseen nouns. We compare three different architectures and observe good performance on classifying word types. In addition, we manually annotate a small corpus for animacy to perform a token-based evaluation of Romanian animacy classification in a naturalistic setting, which reveals limitations of the type-based classification approach.

Automatic Animacy Classification for Romanian Nouns

lrec-main.163

Poster

https://aclanthology.org/2024.lrec-main.164.bib

https://aclanthology.org/2024.lrec-main.164/

@inproceedings{nikolaus-etal-2024-automatic, title = "Automatic Annotation of Grammaticality in Child-Caregiver Conversations", author = "Nikolaus, Mitja and Agrawal, Abhishek and Kaklamanis, Petros and Warstadt, Alex and Fourtassi, Abdellah", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.164", pages = "1832--1844", abstract = "The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Based on these annotations, we train and evaluate a range of NLP models. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels. As a first application and sanity check of this tool, we use the trained models to annotate a corpus almost two orders of magnitude larger than the manually annotated data and verify that children{'}s grammaticality shows a steady increase with age. This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale.", }

The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Based on these annotations, we train and evaluate a range of NLP models. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels. As a first application and sanity check of this tool, we use the trained models to annotate a corpus almost two orders of magnitude larger than the manually annotated data and verify that children{'}s grammaticality shows a steady increase with age. This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale.

Automatic Annotation of Grammaticality in Child-Caregiver Conversations

lrec-main.164

Poster

2403.14208

https://aclanthology.org/2024.lrec-main.165.bib

https://aclanthology.org/2024.lrec-main.165/

@inproceedings{richburg-etal-2024-automatic, title = "Automatic Authorship Analysis in Human-{AI} Collaborative Writing", author = "Richburg, Aquia and Bao, Calvin and Carpuat, Marine", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.165", pages = "1845--1855", abstract = "As the quality of AI-generated text increases with the development of new Large Language Models, people use them to write in a variety of contexts. Human-AI collaborative writing poses a potential challenge for existing AI analysis techniques, which have been primarily tested either on human-written text only, or on samples independently generated by humans and AI. In this work, we investigate the extent to which existing AI detection and authorship analysis models can perform classification on data generated in human-AI collaborative writing sessions. Results show that, for AI text detection in the cowriting setting, classifiers based on authorship embeddings (Rivera-Soto et al., 2021) outperform classifiers used in prior work distinguishing AI vs. human text generated independently. However, these embeddings are not optimal for finer-grained authorship identification tasks: for authorship verification, n-gram based models are more robust to human-AI co-written text, and authorship attribution performance degrades compared to baselines that use human-written text only. Taken together, this suggests that the rise of human-AI co-written text will require adapting AI detection tools and authorship analysis techniques in the near future. We release our code at https://github.com/AARichburg/Human-AI{\_}Authorship{\_}Analysis.", }

As the quality of AI-generated text increases with the development of new Large Language Models, people use them to write in a variety of contexts. Human-AI collaborative writing poses a potential challenge for existing AI analysis techniques, which have been primarily tested either on human-written text only, or on samples independently generated by humans and AI. In this work, we investigate the extent to which existing AI detection and authorship analysis models can perform classification on data generated in human-AI collaborative writing sessions. Results show that, for AI text detection in the cowriting setting, classifiers based on authorship embeddings (Rivera-Soto et al., 2021) outperform classifiers used in prior work distinguishing AI vs. human text generated independently. However, these embeddings are not optimal for finer-grained authorship identification tasks: for authorship verification, n-gram based models are more robust to human-AI co-written text, and authorship attribution performance degrades compared to baselines that use human-written text only. Taken together, this suggests that the rise of human-AI co-written text will require adapting AI detection tools and authorship analysis techniques in the near future. We release our code at https://github.com/AARichburg/Human-AI{\_}Authorship{\_}Analysis.

Automatic Authorship Analysis in Human-AI Collaborative Writing

lrec-main.165

Poster

https://aclanthology.org/2024.lrec-main.166.bib

https://aclanthology.org/2024.lrec-main.166/

@inproceedings{agrawal-etal-2024-automatic, title = "Automatic Coding of Contingency in Child-Caregiver Conversations", author = "Agrawal, Abhishek and Nikolaus, Mitja and Favre, Benoit and Fourtassi, Abdellah", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.166", pages = "1856--1870", abstract = "One of the most important communicative skills children have to learn is to engage in meaningful conversations with people around them. At the heart of this learning lies the mastery of contingency, i.e., the ability to contribute to an ongoing exchange in a relevant fashion (e.g., by staying on topic). Current research on this question relies on the manual annotation of a small sample of children, which limits our ability to draw general conclusions about development. Here, we propose to mitigate the limitations of manual labor by relying on automatic tools for contingency judgment in children{'}s early natural interactions with caregivers. Drawing inspiration from the field of dialogue systems evaluation, we built and compared several automatic classifiers. We found that a Transformer-based pre-trained language model {--} when fine-tuned on a relatively small set of data we annotated manually (around 3,500 turns) {--} provided the best predictions. We used this model to automatically annotate, new and large-scale data, almost two orders of magnitude larger than our fine-tuning set. It was able to replicate existing results and generate new data-driven hypotheses. The broad impact of the work is to provide resources that can help the language development community study communicative development at scale, leading to more robust theories.", }

One of the most important communicative skills children have to learn is to engage in meaningful conversations with people around them. At the heart of this learning lies the mastery of contingency, i.e., the ability to contribute to an ongoing exchange in a relevant fashion (e.g., by staying on topic). Current research on this question relies on the manual annotation of a small sample of children, which limits our ability to draw general conclusions about development. Here, we propose to mitigate the limitations of manual labor by relying on automatic tools for contingency judgment in children{'}s early natural interactions with caregivers. Drawing inspiration from the field of dialogue systems evaluation, we built and compared several automatic classifiers. We found that a Transformer-based pre-trained language model {--} when fine-tuned on a relatively small set of data we annotated manually (around 3,500 turns) {--} provided the best predictions. We used this model to automatically annotate, new and large-scale data, almost two orders of magnitude larger than our fine-tuning set. It was able to replicate existing results and generate new data-driven hypotheses. The broad impact of the work is to provide resources that can help the language development community study communicative development at scale, leading to more robust theories.

Automatic Coding of Contingency in Child-Caregiver Conversations

lrec-main.166

Poster

https://aclanthology.org/2024.lrec-main.167.bib

https://aclanthology.org/2024.lrec-main.167/

@inproceedings{lu-etal-2024-automatic, title = "Automatic Construction of a {C}hinese Review Dataset for Aspect Sentiment Triplet Extraction via Iterative Weak Supervision", author = "Lu, Chia-Wen and Yang, Ching-Wen and Ma, Wei-Yun", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.167", pages = "1871--1882", abstract = "Aspect Sentiment Triplet Extraction (ASTE), introduced in 2020, is a task that involves the extraction of three key elements: target aspects, descriptive opinion spans, and their corresponding sentiment polarity. This process, however, faces a significant hurdle, particularly when applied to Chinese languages, due to the lack of sufficient datasets for model training, largely attributable to the arduous manual labeling process. To address this issue, we present an innovative framework that facilitates the automatic construction of ASTE via Iterative Weak Supervision, negating the need for manual labeling, aided by a discriminator to weed out subpar samples. The objective is to successively improve the quality of this raw data and generate supplementary data. The effectiveness of our approach is underscored by our results, which include the creation of a substantial Chinese review dataset. This dataset encompasses over 60,000 Google restaurant reviews in Chinese and features more than 200,000 extracted triplets. Moreover, we have also established a robust baseline model by leveraging a novel method of weak supervision. Both our dataset and model are openly accessible to the public.", }

Aspect Sentiment Triplet Extraction (ASTE), introduced in 2020, is a task that involves the extraction of three key elements: target aspects, descriptive opinion spans, and their corresponding sentiment polarity. This process, however, faces a significant hurdle, particularly when applied to Chinese languages, due to the lack of sufficient datasets for model training, largely attributable to the arduous manual labeling process. To address this issue, we present an innovative framework that facilitates the automatic construction of ASTE via Iterative Weak Supervision, negating the need for manual labeling, aided by a discriminator to weed out subpar samples. The objective is to successively improve the quality of this raw data and generate supplementary data. The effectiveness of our approach is underscored by our results, which include the creation of a substantial Chinese review dataset. This dataset encompasses over 60,000 Google restaurant reviews in Chinese and features more than 200,000 extracted triplets. Moreover, we have also established a robust baseline model by leveraging a novel method of weak supervision. Both our dataset and model are openly accessible to the public.

Automatic Construction of a Chinese Review Dataset for Aspect Sentiment Triplet Extraction via Iterative Weak Supervision

lrec-main.167

Poster

https://aclanthology.org/2024.lrec-main.168.bib

https://aclanthology.org/2024.lrec-main.168/

@inproceedings{ohno-etal-2024-automatic, title = "Automatic Construction of a Large-Scale Corpus for Geoparsing Using {W}ikipedia Hyperlinks", author = "Ohno, Keyaki and Kameko, Hirotaka and Shirai, Keisuke and Nishimura, Taichi and Mori, Shinsuke", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.168", pages = "1883--1888", abstract = "Geoparsing is the task of estimating the latitude and longitude (coordinates) of location expressions in texts. Geoparsing must deal with the ambiguity of the expressions that indicate multiple locations with the same notation. For evaluating geoparsing systems, several corpora have been proposed in previous work. However, these corpora are small-scale and suffer from the coverage of location expressions on general domains. In this paper, we propose Wikipedia Hyperlink-based Location Linking (WHLL), a novel method to construct a large-scale corpus for geoparsing from Wikipedia articles. WHLL leverages hyperlinks in Wikipedia to annotate multiple location expressions with coordinates. With this method, we constructed the WHLL corpus, a new large-scale corpus for geoparsing. The WHLL corpus consists of 1.3M articles, each containing about 7.8 unique location expressions. 45.6{\%} of location expressions are ambiguous and refer to more than one location with the same notation. In each article, location expressions of the article title and those hyperlinks to other articles are assigned with coordinates. By utilizing hyperlinks, we can accurately assign location expressions with coordinates even with ambiguous location expressions in the texts. Experimental results show that there remains room for improvement by disambiguating location expressions.", }

Geoparsing is the task of estimating the latitude and longitude (coordinates) of location expressions in texts. Geoparsing must deal with the ambiguity of the expressions that indicate multiple locations with the same notation. For evaluating geoparsing systems, several corpora have been proposed in previous work. However, these corpora are small-scale and suffer from the coverage of location expressions on general domains. In this paper, we propose Wikipedia Hyperlink-based Location Linking (WHLL), a novel method to construct a large-scale corpus for geoparsing from Wikipedia articles. WHLL leverages hyperlinks in Wikipedia to annotate multiple location expressions with coordinates. With this method, we constructed the WHLL corpus, a new large-scale corpus for geoparsing. The WHLL corpus consists of 1.3M articles, each containing about 7.8 unique location expressions. 45.6{\%} of location expressions are ambiguous and refer to more than one location with the same notation. In each article, location expressions of the article title and those hyperlinks to other articles are assigned with coordinates. By utilizing hyperlinks, we can accurately assign location expressions with coordinates even with ambiguous location expressions in the texts. Experimental results show that there remains room for improvement by disambiguating location expressions.

Automatic Construction of a Large-Scale Corpus for Geoparsing Using Wikipedia Hyperlinks

lrec-main.168

Poster

2403.16483

https://aclanthology.org/2024.lrec-main.169.bib

https://aclanthology.org/2024.lrec-main.169/

@inproceedings{ge-etal-2024-automatic, title = "Automatic Data Visualization Generation from {C}hinese Natural Language Questions", author = "Ge, Yan and Wei, Victor Junqiu and Song, Yuanfeng and Zhang, Jason Chen and Wong, Raymond Chi-Wing", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.169", pages = "1889--1898", abstract = "Data visualization has emerged as an effective tool for getting insights from massive datasets. Due to the hardness of manipulating the programming languages of data visualization, automatic data visualization generation from natural languages (Text-to-Vis) is becoming increasingly popular. Despite the plethora of research effort on the English Text-to-Vis, studies have yet to be conducted on data visualization generation from questions in Chinese. Motivated by this, we propose a Chinese Text-to-Vis dataset in the paper and demonstrate our first attempt to tackle this problem. Our model integrates multilingual BERT as the encoder, boosts the cross-lingual ability, and infuses the n-gram information into our word representation learning. Our experimental results show that our dataset is challenging and deserves further research.", }

Data visualization has emerged as an effective tool for getting insights from massive datasets. Due to the hardness of manipulating the programming languages of data visualization, automatic data visualization generation from natural languages (Text-to-Vis) is becoming increasingly popular. Despite the plethora of research effort on the English Text-to-Vis, studies have yet to be conducted on data visualization generation from questions in Chinese. Motivated by this, we propose a Chinese Text-to-Vis dataset in the paper and demonstrate our first attempt to tackle this problem. Our model integrates multilingual BERT as the encoder, boosts the cross-lingual ability, and infuses the n-gram information into our word representation learning. Our experimental results show that our dataset is challenging and deserves further research.

Automatic Data Visualization Generation from Chinese Natural Language Questions

lrec-main.169

Poster

2309.07650

https://aclanthology.org/2024.lrec-main.170.bib

https://aclanthology.org/2024.lrec-main.170/

@inproceedings{yamaguchi-etal-2024-automatic, title = "Automatic Decomposition of Text Editing Examples into Primitive Edit Operations: Toward Analytic Evaluation of Editing Systems", author = "Yamaguchi, Daichi and Miyata, Rei and Fujita, Atsushi and Kajiwara, Tomoyuki and Sato, Satoshi", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.170", pages = "1899--1914", abstract = "This paper presents our work on a task of automatic decomposition of text editing examples into primitive edit operations. Toward a detailed analysis of the behavior of text editing systems, identification of fine-grained edit operations performed by the systems is essential. Given a pair of source and edited sentences, the goal of our task is to generate a non-redundant sequence of primitive edit operations, i.e., the semantically minimal edit operations preserving grammaticality, that iteratively converts the source sentence to the edited sentence. First, we formalize this task, explaining its significant features and specifying the constraints that primitive edit operations should satisfy. Then, we propose a method to automate this task, which consists of two steps: generation of an edit operation lattice and selection of an optimal path. To obtain a wide range of edit operation candidates in the first step, we combine a phrase aligner and a large language model. Experimental results show that our method perfectly decomposes 44{\%} and 64{\%} of editing examples in the text simplification and machine translation post-editing datasets, respectively. Detailed analyses also provide insights into the difficulties of this task, suggesting directions for improvement.", }

This paper presents our work on a task of automatic decomposition of text editing examples into primitive edit operations. Toward a detailed analysis of the behavior of text editing systems, identification of fine-grained edit operations performed by the systems is essential. Given a pair of source and edited sentences, the goal of our task is to generate a non-redundant sequence of primitive edit operations, i.e., the semantically minimal edit operations preserving grammaticality, that iteratively converts the source sentence to the edited sentence. First, we formalize this task, explaining its significant features and specifying the constraints that primitive edit operations should satisfy. Then, we propose a method to automate this task, which consists of two steps: generation of an edit operation lattice and selection of an optimal path. To obtain a wide range of edit operation candidates in the first step, we combine a phrase aligner and a large language model. Experimental results show that our method perfectly decomposes 44{\%} and 64{\%} of editing examples in the text simplification and machine translation post-editing datasets, respectively. Detailed analyses also provide insights into the difficulties of this task, suggesting directions for improvement.

Automatic Decomposition of Text Editing Examples into Primitive Edit Operations: Toward Analytic Evaluation of Editing Systems

lrec-main.170

Poster

https://aclanthology.org/2024.lrec-main.171.bib

https://aclanthology.org/2024.lrec-main.171/

@inproceedings{callegari-etal-2024-automatic, title = "Automatic Extraction of Language-Specific Biomarkers of Healthy Aging in {I}celandic", author = "Callegari, Elena and Nowenstein, Iris Edda and Kristj{\'a}nsd{\'o}ttir, Ingunn J{\'o}hanna and Ingason, Anton Karl", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.171", pages = "1915--1924", abstract = "This study examines the influence of task type and healthy aging on various automatically extracted part-of-speech features in Icelandic. We administered three language tasks to participants aged 60{--}80: picture description, trip planning, and description of one{'}s childhood home. Our findings reveal significant task effects on 11 out of 14 linguistic variables studied, highlighting the substantial influence of sampling methods on language production. Among the variables showing statistically significant task effects, we find the rate of the genitive and subjunctive, variables which can only be studied in morphologically richer languages like Icelandic. On the other hand, rates of pronouns, adverbs, and prepositions remained stable across task types. Aging effects were more subtle, being evident in 3 of the 14 variables, including an interaction with task type for dative case marking. These findings underscore the significance of task selection in studies targeting linguistic features but also emphasize the need to examine languages other than English to fully understand the effects of aging on language production. Additionally, the results have clinical implications: understanding healthy aging{'}s impact on language can help us better identify and study changes caused by Alzheimer{'}s Disease in older adults{'} speech.", }

This study examines the influence of task type and healthy aging on various automatically extracted part-of-speech features in Icelandic. We administered three language tasks to participants aged 60{--}80: picture description, trip planning, and description of one{'}s childhood home. Our findings reveal significant task effects on 11 out of 14 linguistic variables studied, highlighting the substantial influence of sampling methods on language production. Among the variables showing statistically significant task effects, we find the rate of the genitive and subjunctive, variables which can only be studied in morphologically richer languages like Icelandic. On the other hand, rates of pronouns, adverbs, and prepositions remained stable across task types. Aging effects were more subtle, being evident in 3 of the 14 variables, including an interaction with task type for dative case marking. These findings underscore the significance of task selection in studies targeting linguistic features but also emphasize the need to examine languages other than English to fully understand the effects of aging on language production. Additionally, the results have clinical implications: understanding healthy aging{'}s impact on language can help us better identify and study changes caused by Alzheimer{'}s Disease in older adults{'} speech.

Automatic Extraction of Language-Specific Biomarkers of Healthy Aging in Icelandic

lrec-main.171

Poster

https://aclanthology.org/2024.lrec-main.172.bib

https://aclanthology.org/2024.lrec-main.172/

@inproceedings{laarmann-quante-etal-2024-automatic, title = "Automatic Extraction of Nominal Phrases from {G}erman Learner Texts of Different Proficiency Levels", author = {Laarmann-Quante, Ronja and M{\"u}ller, Marco and Belke, Eva}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.172", pages = "1925--1931", abstract = "Correctly inflecting determiners and adjectives so that they agree with the noun in nominal phrases (NPs) is a big challenge for learners of German. Given the increasing number of available learner corpora, a large-scale corpus-based study on the acquisition of this aspect of German morphosyntax would be desirable. In this paper, we present a pilot study in which we investigate how well nouns, their grammatical heads and the dependents that have to agree with the noun can be extracted automatically via dependency parsing. For six samples of the German learner corpus MERLIN (one per proficiency level), we found that in spite of many ungrammatical sentences in texts of low proficiency levels, human annotators find only few true ambiguities that would make the extraction of NPs and their heads infeasible. The automatic parsers, however, perform rather poorly on extracting the relevant elements for texts on CEFR levels A1-B1 ({\textless} 70{\%}) but quite well from level B2 onwards ( 90{\%}). We discuss the sources of errors and how performance could potentially be increased in the future.", }

Correctly inflecting determiners and adjectives so that they agree with the noun in nominal phrases (NPs) is a big challenge for learners of German. Given the increasing number of available learner corpora, a large-scale corpus-based study on the acquisition of this aspect of German morphosyntax would be desirable. In this paper, we present a pilot study in which we investigate how well nouns, their grammatical heads and the dependents that have to agree with the noun can be extracted automatically via dependency parsing. For six samples of the German learner corpus MERLIN (one per proficiency level), we found that in spite of many ungrammatical sentences in texts of low proficiency levels, human annotators find only few true ambiguities that would make the extraction of NPs and their heads infeasible. The automatic parsers, however, perform rather poorly on extracting the relevant elements for texts on CEFR levels A1-B1 ({\textless} 70{\%}) but quite well from level B2 onwards ( 90{\%}). We discuss the sources of errors and how performance could potentially be increased in the future.

Automatic Extraction of Nominal Phrases from German Learner Texts of Different Proficiency Levels

lrec-main.172

Poster

https://aclanthology.org/2024.lrec-main.173.bib

https://aclanthology.org/2024.lrec-main.173/

@inproceedings{heinrich-etal-2024-automatic, title = "Automatic Identification of {COVID}-19-Related Conspiracy Narratives in {G}erman Telegram Channels and Chats", author = {Heinrich, Philipp and Blombach, Andreas and Doan Dang, Bao Minh and Zilio, Leonardo and Havenstein, Linda and Dykes, Nathan and Evert, Stephanie and Sch{\"a}fer, Fabian}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.173", pages = "1932--1943", abstract = "We are concerned with mapping the discursive landscape of conspiracy narratives surrounding the COVID-19 pandemic. In the present study, we analyse a corpus of more than 1,000 German Telegram posts tagged with 14 fine-grained conspiracy narrative labels by three independent annotators. Since emerging narratives on social media are short-lived and notoriously hard to track, we experiment with different state-of-the-art approaches to few-shot and zero-shot text classification. We report performance in terms of ROC-AUC and in terms of optimal F1, and compare fine-tuned methods with off-the-shelf approaches and human performance.", }

We are concerned with mapping the discursive landscape of conspiracy narratives surrounding the COVID-19 pandemic. In the present study, we analyse a corpus of more than 1,000 German Telegram posts tagged with 14 fine-grained conspiracy narrative labels by three independent annotators. Since emerging narratives on social media are short-lived and notoriously hard to track, we experiment with different state-of-the-art approaches to few-shot and zero-shot text classification. We report performance in terms of ROC-AUC and in terms of optimal F1, and compare fine-tuned methods with off-the-shelf approaches and human performance.

Automatic Identification of COVID-19-Related Conspiracy Narratives in German Telegram Channels and Chats

lrec-main.173

Poster

https://aclanthology.org/2024.lrec-main.174.bib

https://aclanthology.org/2024.lrec-main.174/

@inproceedings{jansen-van-vuren-etal-2024-automatic, title = "Automatic Partitioning of a Code-Switched Speech Corpus Using Mixed-Integer Programming", author = {Jansen van V{\"u}ren, Joshua Miles and de Wet, Febe and Niesler, Thomas}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.174", pages = "1944--1952", abstract = "Defining training, development and test set partitions for speech corpora is usually accomplished by hand. However, for the dataset under investigation, which contains a large number of speakers, eight different languages and code-switching between all the languages, this style of partitioning is not feasible. Therefore, we view the partitioning task as a resource allocation problem and propose to solve it automatically and optimally by the application of mixed-integer linear programming. Using this approach, we are able to partition a new 41.6-hour multilingual corpus of code-switched speech into training, development and testing partitions while maintaining a fixed number of speakers and a specific amount of code-switched speech in the development and test partitions. For this newly partitioned corpus, we present baseline speech recognition results using a state-of-the-art multilingual transformer model (Wav2Vec2-XLS-R) and show that the exclusion of very short utterances ({\textless}1s) results in substantially improved speech recognition performance.", }

Defining training, development and test set partitions for speech corpora is usually accomplished by hand. However, for the dataset under investigation, which contains a large number of speakers, eight different languages and code-switching between all the languages, this style of partitioning is not feasible. Therefore, we view the partitioning task as a resource allocation problem and propose to solve it automatically and optimally by the application of mixed-integer linear programming. Using this approach, we are able to partition a new 41.6-hour multilingual corpus of code-switched speech into training, development and testing partitions while maintaining a fixed number of speakers and a specific amount of code-switched speech in the development and test partitions. For this newly partitioned corpus, we present baseline speech recognition results using a state-of-the-art multilingual transformer model (Wav2Vec2-XLS-R) and show that the exclusion of very short utterances ({\textless}1s) results in substantially improved speech recognition performance.

Automatic Partitioning of a Code-Switched Speech Corpus Using Mixed-Integer Programming

lrec-main.174

Poster

https://aclanthology.org/2024.lrec-main.175.bib

https://aclanthology.org/2024.lrec-main.175/

@inproceedings{perez-enriquez-etal-2024-automatic, title = "Automatic Punctuation Model for {S}panish Live Transcriptions", author = "Perez-Enriquez, Mario and Masiello-Ruiz, Jose Manuel and Lopez-Cuadrado, Jose Luis and Gonzalez-Carrasco, Israel and Martinez-Fernandez, Paloma and Ruiz-Mezcua, Belen", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.175", pages = "1953--1958", abstract = "With the widespread adoption of automatic transcription tools, acquiring speech transcriptions within seconds has become a reality. Nonetheless, many of these tools yield unpunctuated outputs, potentially incurring additional costs. This paper presents a novel approach to integrating punctuation into the transcriptions generated by such automatic tools, specifically focusing on Spanish-speaking contexts. Leveraging the RoBERTa-bne model pre-trained with data from the Spanish National Library, our training proposal is augmented with additional corpora to enhance performance on less common punctuation marks, such as question marks. Also, the proposed model has been trained through fine-tuning pre-trained models, involving adjustments for token classification and using SoftMax to identify the highest probability token. The proposed model obtains promising results when compared with other Spanish reference paper models. Ultimately, this model aims to facilitate punctuation on live transcriptions seamlessly and accurately. The proposed model will be applied to a real-case education project to improve the readability of the transcriptions.", }

With the widespread adoption of automatic transcription tools, acquiring speech transcriptions within seconds has become a reality. Nonetheless, many of these tools yield unpunctuated outputs, potentially incurring additional costs. This paper presents a novel approach to integrating punctuation into the transcriptions generated by such automatic tools, specifically focusing on Spanish-speaking contexts. Leveraging the RoBERTa-bne model pre-trained with data from the Spanish National Library, our training proposal is augmented with additional corpora to enhance performance on less common punctuation marks, such as question marks. Also, the proposed model has been trained through fine-tuning pre-trained models, involving adjustments for token classification and using SoftMax to identify the highest probability token. The proposed model obtains promising results when compared with other Spanish reference paper models. Ultimately, this model aims to facilitate punctuation on live transcriptions seamlessly and accurately. The proposed model will be applied to a real-case education project to improve the readability of the transcriptions.

Automatic Punctuation Model for Spanish Live Transcriptions

lrec-main.175

Poster

https://aclanthology.org/2024.lrec-main.176.bib

https://aclanthology.org/2024.lrec-main.176/

@inproceedings{lebourdais-etal-2024-automatic, title = "Automatic Speech Interruption Detection: Analysis, Corpus, and System", author = "Lebourdais, Martin and Tahon, Marie and Laurent, Antoine and Meignier, Sylvain", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.176", pages = "1959--1968", abstract = "Interruption detection is a new yet challenging task in the field of speech processing. This article presents a comprehensive study on automatic speech interruption detection, from the definition of this task, the assembly of a specialized corpus, and the development of an initial baseline system. We provide three main contributions: Firstly, we define the task, taking into account the nuanced nature of interruptions within spontaneous conversations. Secondly, we introduce a new corpus of conversational data, annotated for interruptions, to facilitate research in this domain. This corpus serves as a valuable resource for evaluating and advancing interruption detection techniques. Lastly, we present a first baseline system, which use speech processing methods to automatically identify interruptions in speech with promising results. In this article, we derivate from theoretical notions of interruption to build a simplification of this notion based on overlapped speech detection. Our findings can not only serve as a foundation for further research in the field but also provide a benchmark for assessing future advancements in automatic speech interruption detection.", }

Interruption detection is a new yet challenging task in the field of speech processing. This article presents a comprehensive study on automatic speech interruption detection, from the definition of this task, the assembly of a specialized corpus, and the development of an initial baseline system. We provide three main contributions: Firstly, we define the task, taking into account the nuanced nature of interruptions within spontaneous conversations. Secondly, we introduce a new corpus of conversational data, annotated for interruptions, to facilitate research in this domain. This corpus serves as a valuable resource for evaluating and advancing interruption detection techniques. Lastly, we present a first baseline system, which use speech processing methods to automatically identify interruptions in speech with promising results. In this article, we derivate from theoretical notions of interruption to build a simplification of this notion based on overlapped speech detection. Our findings can not only serve as a foundation for further research in the field but also provide a benchmark for assessing future advancements in automatic speech interruption detection.

Automatic Speech Interruption Detection: Analysis, Corpus, and System

lrec-main.176

Poster

https://aclanthology.org/2024.lrec-main.177.bib

https://aclanthology.org/2024.lrec-main.177/

@inproceedings{morcillo-etal-2024-automatic, title = "Automatic Speech Recognition for {G}ascon and Languedocian Variants of {O}ccitan", author = {Morcillo, I{\~n}igo and Leturia, Igor and Corral, Ander and Sarasola, Xabier and Barret, Micha{\"e}l and S{\'e}guier, Aure and Daz{\'e}as, Benaset}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.177", pages = "1969--1978", abstract = "This paper describes different approaches for developing, for the first time, an automatic speech recognition system for two of the main dialects of Occitan, namely Gascon and Languedocian, and the results obtained in them. The difficulty of the task lies in the fact that Occitan is a less-resourced language. Although a great effort has been made to collect or create corpora of each variant (transcribed speech recordings for the acoustic models and two text corpora for the language models), the sizes of the corpora obtained are far from those of successful systems reported in the literature, and thus we have tested different techniques to compensate for the lack of resources. We have developed classical systems using Kaldi, creating an acoustic model for each variant and also creating language models from the collected corpora and from machine translated texts. We have also tried fine-tuning a Whisper model with our speech corpora. We report word error rates of 20.86 for Gascon and 13.52 for Languedocian with the Kaldi systems and 16.37 for Gascon and 11.74 for Languedocian with Whisper.", }

This paper describes different approaches for developing, for the first time, an automatic speech recognition system for two of the main dialects of Occitan, namely Gascon and Languedocian, and the results obtained in them. The difficulty of the task lies in the fact that Occitan is a less-resourced language. Although a great effort has been made to collect or create corpora of each variant (transcribed speech recordings for the acoustic models and two text corpora for the language models), the sizes of the corpora obtained are far from those of successful systems reported in the literature, and thus we have tested different techniques to compensate for the lack of resources. We have developed classical systems using Kaldi, creating an acoustic model for each variant and also creating language models from the collected corpora and from machine translated texts. We have also tried fine-tuning a Whisper model with our speech corpora. We report word error rates of 20.86 for Gascon and 13.52 for Languedocian with the Kaldi systems and 16.37 for Gascon and 11.74 for Languedocian with Whisper.

Automatic Speech Recognition for Gascon and Languedocian Variants of Occitan

lrec-main.177

Poster

https://aclanthology.org/2024.lrec-main.178.bib

https://aclanthology.org/2024.lrec-main.178/

@inproceedings{park-etal-2024-automatic, title = "Automatic Speech Recognition System-Independent Word Error Rate Estimation", author = "Park, Chanho and Chen, Mingjie and Hain, Thomas", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.178", pages = "1979--1987", abstract = "Word error rate (WER) is a metric used to evaluate the quality of transcriptions produced by Automatic Speech Recognition (ASR) systems. In many applications, it is of interest to estimate WER given a pair of a speech utterance and a transcript. Previous work on WER estimation focused on building models that are trained with a specific ASR system in mind (referred to as ASR system-dependent). These are also domain-dependent and inflexible in real-world applications. In this paper, a hypothesis generation method for ASR System-Independent WER estimation (SIWE) is proposed. In contrast to prior work, the WER estimators are trained using data that simulates ASR system output. Hypotheses are generated using phonetically similar or linguistically more likely alternative words. In WER estimation experiments, the proposed method reaches a similar performance to ASR system-dependent WER estimators on in-domain data and achieves state-of-the-art performance on out-of-domain data. On the out-of-domain data, the SIWE model outperformed the baseline estimators in root mean square error and Pearson correlation coefficient by relative 17.58{\%} and 18.21{\%}, respectively, on Switchboard and CALLHOME. The performance was further improved when the WER of the training set was close to the WER of the evaluation dataset.", }

Word error rate (WER) is a metric used to evaluate the quality of transcriptions produced by Automatic Speech Recognition (ASR) systems. In many applications, it is of interest to estimate WER given a pair of a speech utterance and a transcript. Previous work on WER estimation focused on building models that are trained with a specific ASR system in mind (referred to as ASR system-dependent). These are also domain-dependent and inflexible in real-world applications. In this paper, a hypothesis generation method for ASR System-Independent WER estimation (SIWE) is proposed. In contrast to prior work, the WER estimators are trained using data that simulates ASR system output. Hypotheses are generated using phonetically similar or linguistically more likely alternative words. In WER estimation experiments, the proposed method reaches a similar performance to ASR system-dependent WER estimators on in-domain data and achieves state-of-the-art performance on out-of-domain data. On the out-of-domain data, the SIWE model outperformed the baseline estimators in root mean square error and Pearson correlation coefficient by relative 17.58{\%} and 18.21{\%}, respectively, on Switchboard and CALLHOME. The performance was further improved when the WER of the training set was close to the WER of the evaluation dataset.

Automatic Speech Recognition System-Independent Word Error Rate Estimation

lrec-main.178

Poster

2404.16743

https://aclanthology.org/2024.lrec-main.179.bib

https://aclanthology.org/2024.lrec-main.179/

@inproceedings{thierauf-etal-2024-automating, title = "Automating Dataset Production Using Generative Text and Image Models", author = "Thierauf, Christopher and Abrams, Mitchell and Scheutz, Matthias", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.179", pages = "1988--1995", abstract = "Practical and ethical dataset collection remains a challenge blocking many empirical methods in natural language processing, resulting in a lack of benchmarks or data on which to test hypotheses. We propose a solution to some of these areas by presenting a pipeline to reduce the research burden of producing image and text datasets when datasets may not exist. Our approach, with accompanying software tools, involves (1) generating text with LLMs; (2) creating accompanying image vignettes with text{--}to{--}image transformers; and (3) low-cost human validation. Based on existing literature that has struggled with quantitative evaluation (due to difficulty of data collection), we present the creation of 3 relevant datasets, and conduct a user study that demonstrates this approach is able to aid researchers in obtaining previously-challenging datasets. We provide sample data generated with this technique, the source code used to produce it, and discuss applicability and limitations.", }

Practical and ethical dataset collection remains a challenge blocking many empirical methods in natural language processing, resulting in a lack of benchmarks or data on which to test hypotheses. We propose a solution to some of these areas by presenting a pipeline to reduce the research burden of producing image and text datasets when datasets may not exist. Our approach, with accompanying software tools, involves (1) generating text with LLMs; (2) creating accompanying image vignettes with text{--}to{--}image transformers; and (3) low-cost human validation. Based on existing literature that has struggled with quantitative evaluation (due to difficulty of data collection), we present the creation of 3 relevant datasets, and conduct a user study that demonstrates this approach is able to aid researchers in obtaining previously-challenging datasets. We provide sample data generated with this technique, the source code used to produce it, and discuss applicability and limitations.

Automating Dataset Production Using Generative Text and Image Models

lrec-main.179

Poster

https://aclanthology.org/2024.lrec-main.180.bib

https://aclanthology.org/2024.lrec-main.180/

@inproceedings{feng-etal-2024-autonomous, title = "Autonomous Aspect-Image Instruction a2{II}: {Q}-Former Guided Multimodal Sentiment Classification", author = "Feng, Junjia and Lin, Mingqian and Shang, Lin and Gao, Xiaoying", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.180", pages = "1996--2005", abstract = "Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings.", }

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings.

Autonomous Aspect-Image Instruction a2II: Q-Former Guided Multimodal Sentiment Classification

lrec-main.180

Poster

https://aclanthology.org/2024.lrec-main.181.bib

https://aclanthology.org/2024.lrec-main.181/

@inproceedings{wang-etal-2024-auxiliary, title = "Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification", author = "Wang, Xindi and Mercer, Robert E. and Rudzicz, Frank", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.181", pages = "2006--2016", abstract = "The International Classification of Diseases (ICD) is an authoritative medical classification system of different diseases and conditions for clinical and management purposes. ICD indexing aims to assign a subset of ICD codes to a medical record. Since human coding is labour-intensive and error-prone, many studies employ machine learning techniques to automate the coding process. ICD coding is a challenging task, as it needs to assign multiple codes to each medical document from an extremely large hierarchically organized collection. In this paper, we propose a novel approach for ICD indexing that adopts three ideas: (1) we use a multi-level deep dilated residual convolution encoder to aggregate the information from the clinical notes and learn document representations across different lengths of the texts; (2) we formalize the task of ICD classification with auxiliary knowledge of the medical records, which incorporates not only the clinical texts but also different clinical code terminologies and drug prescriptions for better inferring the ICD codes; and (3) we introduce a graph convolutional network to leverage the co-occurrence patterns among ICD codes, aiming to enhance the quality of label representations. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.", }

The International Classification of Diseases (ICD) is an authoritative medical classification system of different diseases and conditions for clinical and management purposes. ICD indexing aims to assign a subset of ICD codes to a medical record. Since human coding is labour-intensive and error-prone, many studies employ machine learning techniques to automate the coding process. ICD coding is a challenging task, as it needs to assign multiple codes to each medical document from an extremely large hierarchically organized collection. In this paper, we propose a novel approach for ICD indexing that adopts three ideas: (1) we use a multi-level deep dilated residual convolution encoder to aggregate the information from the clinical notes and learn document representations across different lengths of the texts; (2) we formalize the task of ICD classification with auxiliary knowledge of the medical records, which incorporates not only the clinical texts but also different clinical code terminologies and drug prescriptions for better inferring the ICD codes; and (3) we introduce a graph convolutional network to leverage the co-occurrence patterns among ICD codes, aiming to enhance the quality of label representations. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.

Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification

lrec-main.181

Poster

2405.19084

https://aclanthology.org/2024.lrec-main.182.bib

https://aclanthology.org/2024.lrec-main.182/

@inproceedings{arana-etal-2024-virtual, title = "A Virtual Patient Dialogue System Based on Question-Answering on Clinical Records", author = "Arana, Janire and Idoyaga, Mikel and Urruela, Maitane and Espina, Elisa and Atutxa Salazar, Aitziber and Gojenola, Koldo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.182", pages = "2017--2027", abstract = "In this work we present two datasets for the development of virtual patients and the first evaluation results. We firstly introduce a Spanish corpus of medical dialogue questions annotated with intents, built upon prior research in French. We also propose a second dataset of dialogues using a novel annotation approach that involves doctor questions, patient answers, and corresponding clinical records, organized as triples of the form (clinical report, question, patient answer). This way, the doctor-patient conversation is modeled as a question-answering system that tries to find responses to questions taking a clinical record as input. This approach can help to eliminate the need for manually structured patient records, as commonly used in previous studies, thereby expanding the pool of diverse virtual patients available. Leveraging these annotated corpora, we develop and assess an automatic system designed to answer medical dialogue questions posed by medical students to simulated patients in medical exams. Our approach demonstrates robust generalization, relying solely on medical records to generate new patient cases. The two datasets and the code will be freely available for the research community.", }

In this work we present two datasets for the development of virtual patients and the first evaluation results. We firstly introduce a Spanish corpus of medical dialogue questions annotated with intents, built upon prior research in French. We also propose a second dataset of dialogues using a novel annotation approach that involves doctor questions, patient answers, and corresponding clinical records, organized as triples of the form (clinical report, question, patient answer). This way, the doctor-patient conversation is modeled as a question-answering system that tries to find responses to questions taking a clinical record as input. This approach can help to eliminate the need for manually structured patient records, as commonly used in previous studies, thereby expanding the pool of diverse virtual patients available. Leveraging these annotated corpora, we develop and assess an automatic system designed to answer medical dialogue questions posed by medical students to simulated patients in medical exams. Our approach demonstrates robust generalization, relying solely on medical records to generate new patient cases. The two datasets and the code will be freely available for the research community.

A Virtual Patient Dialogue System Based on Question-Answering on Clinical Records

lrec-main.182

Poster

https://aclanthology.org/2024.lrec-main.183.bib

https://aclanthology.org/2024.lrec-main.183/

@inproceedings{amigo-etal-2024-web, title = "A Web Portal about the State of the Art of {NLP} Tasks in {S}panish", author = "Amig{\'o}, Enrique and Carrillo-de-Albornoz, Jorge and Fern{\'a}ndez, Andr{\'e}s and Gonzalo, Julio and Marco, Guillermo and Morante, Roser and Plaza, Laura and Pedrosa, Jacobo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.183", pages = "2028--2038", abstract = "This paper presents a new web portal with information about the state of the art of natural language processing tasks in Spanish. It provides information about forums, competitions, tasks and datasets in Spanish, that would otherwise be spread in multiple articles and web sites. The portal consists of overview pages where information can be searched for and filtered by several criteria and individual pages with detailed information and hyperlinks to facilitate navigation. Information has been manually curated from publications that describe competitions and NLP tasks from 2013 until 2023 and will be updated as new tasks appear. A total of 185 tasks and 128 datasets from 94 competitions have been introduced.", }

This paper presents a new web portal with information about the state of the art of natural language processing tasks in Spanish. It provides information about forums, competitions, tasks and datasets in Spanish, that would otherwise be spread in multiple articles and web sites. The portal consists of overview pages where information can be searched for and filtered by several criteria and individual pages with detailed information and hyperlinks to facilitate navigation. Information has been manually curated from publications that describe competitions and NLP tasks from 2013 until 2023 and will be updated as new tasks appear. A total of 185 tasks and 128 datasets from 94 competitions have been introduced.

A Web Portal about the State of the Art of NLP Tasks in Spanish

lrec-main.183

Poster

https://aclanthology.org/2024.lrec-main.184.bib

https://aclanthology.org/2024.lrec-main.184/

@inproceedings{lendvai-etal-2024-workflow, title = "A Workflow for {HTR}-Postprocessing, Labeling and Classifying Diachronic and Regional Variation in Pre-{M}odern {S}lavic Texts", author = "Lendvai, Piroska and van Gompel, Maarten and Jouravel, Anna and Renje, Elena and Reichel, Uwe and Rabus, Achim and Arnold, Eckhart", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.184", pages = "2039--2048", abstract = "We describe ongoing work for developing a workflow for the applied use case of classifying diachronic and regional language variation in Pre-Modern Slavic texts. The data were obtained via handwritten text recognition (HTR) on medieval manuscripts and printings and partly by manual transcription. Our goal is to develop a workflow for such historical language data, covering HTR-postprocessing, annotating and classifying the digitized texts. We test and adapt existing language resources to fit the pipeline with low-barrier tooling, accessible for Humanists with limited experience in research data infrastructures, computational analysis or advanced methods of natural language processing (NLP). The workflow starts by addressing ground truth (GT) data creation for diagnosing and correcting HTR errors via string metrics and data-driven methods. On GT and on HTR data, we subsequently show classification results using transfer learning on sentence-level text snippets. Next, we report on our token-level data labeling efforts. Each step of the workflow is complemented with describing current limitations and our corresponding work in progress.", }

We describe ongoing work for developing a workflow for the applied use case of classifying diachronic and regional language variation in Pre-Modern Slavic texts. The data were obtained via handwritten text recognition (HTR) on medieval manuscripts and printings and partly by manual transcription. Our goal is to develop a workflow for such historical language data, covering HTR-postprocessing, annotating and classifying the digitized texts. We test and adapt existing language resources to fit the pipeline with low-barrier tooling, accessible for Humanists with limited experience in research data infrastructures, computational analysis or advanced methods of natural language processing (NLP). The workflow starts by addressing ground truth (GT) data creation for diagnosing and correcting HTR errors via string metrics and data-driven methods. On GT and on HTR data, we subsequently show classification results using transfer learning on sentence-level text snippets. Next, we report on our token-level data labeling efforts. Each step of the workflow is complemented with describing current limitations and our corresponding work in progress.

A Workflow for HTR-Postprocessing, Labeling and Classifying Diachronic and Regional Variation in Pre-Modern Slavic Texts

lrec-main.184

Poster

https://aclanthology.org/2024.lrec-main.185.bib

https://aclanthology.org/2024.lrec-main.185/

@inproceedings{labrak-etal-2024-zero, title = "A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks", author = "Labrak, Yanis and Rouvier, Mickael and Dufour, Richard", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.185", pages = "2049--2066", abstract = "The recent emergence of Large Language Models (LLMs) has enabled significant advances in the field of Natural Language Processing (NLP). While these new models have demonstrated superior performance on various tasks, their application and potential are still underexplored, both in terms of the diversity of tasks they can handle and their domain of application. In this context, we evaluate four state-of-the-art instruction-tuned LLMs (ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca) on a set of 13 real-world clinical and biomedical NLP tasks in English, including named-entity recognition (NER), question-answering (QA), relation extraction (RE), and more. Our overall results show that these evaluated LLMs approach the performance of state-of-the-art models in zero- and few-shot scenarios for most tasks, particularly excelling in the QA task, even though they have never encountered examples from these tasks before. However, we also observe that the classification and RE tasks fall short of the performance achievable with specifically trained models designed for the medical field, such as PubMedBERT. Finally, we note that no single LLM outperforms all others across all studied tasks, with some models proving more suitable for certain tasks than others.", }

The recent emergence of Large Language Models (LLMs) has enabled significant advances in the field of Natural Language Processing (NLP). While these new models have demonstrated superior performance on various tasks, their application and potential are still underexplored, both in terms of the diversity of tasks they can handle and their domain of application. In this context, we evaluate four state-of-the-art instruction-tuned LLMs (ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca) on a set of 13 real-world clinical and biomedical NLP tasks in English, including named-entity recognition (NER), question-answering (QA), relation extraction (RE), and more. Our overall results show that these evaluated LLMs approach the performance of state-of-the-art models in zero- and few-shot scenarios for most tasks, particularly excelling in the QA task, even though they have never encountered examples from these tasks before. However, we also observe that the classification and RE tasks fall short of the performance achievable with specifically trained models designed for the medical field, such as PubMedBERT. Finally, we note that no single LLM outperforms all others across all studied tasks, with some models proving more suitable for certain tasks than others.

A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks

lrec-main.185

Poster

2307.12114

https://aclanthology.org/2024.lrec-main.186.bib

https://aclanthology.org/2024.lrec-main.186/

@inproceedings{du-etal-2024-backdoor, title = "Backdoor {NLP} Models via {AI}-Generated Text", author = "Du, Wei and Ju, Tianjie and Ren, Ge and Li, GaoLei and Liu, Gongshen", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.186", pages = "2067--2079", abstract = "Backdoor attacks pose a critical security threat to natural language processing (NLP) models by establishing covert associations between trigger patterns and target labels without affecting normal accuracy. Existing attacks usually disregard fluency and semantic fidelity of poisoned text, rendering the malicious data easily detectable. However, text generation models can produce coherent and content-relevant text given prompts. Moreover, potential differences between human-written and AI-generated text may be captured by NLP models while being imperceptible to humans. More insidious threats could arise if attackers leverage latent features of AI-generated text as trigger patterns. We comprehensively investigate backdoor attacks on NLP models using AI-generated poisoned text obtained via continued writing or paraphrasing, exploring three attack scenarios: data, model and pre-training. For data poisoning, we fine-tune generators with attribute control to enhance the attack performance. For model poisoning, we leverage downstream tasks to derive specialized generators. For pre-training poisoning, we train multiple attribute-based generators and align their generated text with pre-defined vectors, enabling task-agnostic migration attacks. Experiments demonstrate that our method achieves effective attacks while maintaining fluency and semantic similarity across all scenarios. We hope this work can raise awareness of the security risks hidden in AI-generated text.", }

Backdoor attacks pose a critical security threat to natural language processing (NLP) models by establishing covert associations between trigger patterns and target labels without affecting normal accuracy. Existing attacks usually disregard fluency and semantic fidelity of poisoned text, rendering the malicious data easily detectable. However, text generation models can produce coherent and content-relevant text given prompts. Moreover, potential differences between human-written and AI-generated text may be captured by NLP models while being imperceptible to humans. More insidious threats could arise if attackers leverage latent features of AI-generated text as trigger patterns. We comprehensively investigate backdoor attacks on NLP models using AI-generated poisoned text obtained via continued writing or paraphrasing, exploring three attack scenarios: data, model and pre-training. For data poisoning, we fine-tune generators with attribute control to enhance the attack performance. For model poisoning, we leverage downstream tasks to derive specialized generators. For pre-training poisoning, we train multiple attribute-based generators and align their generated text with pre-defined vectors, enabling task-agnostic migration attacks. Experiments demonstrate that our method achieves effective attacks while maintaining fluency and semantic similarity across all scenarios. We hope this work can raise awareness of the security risks hidden in AI-generated text.

Backdoor NLP Models via AI-Generated Text

lrec-main.186

Poster

https://aclanthology.org/2024.lrec-main.187.bib

https://aclanthology.org/2024.lrec-main.187/

@inproceedings{dargis-etal-2024-balsutalka, title = "{B}alsu{T}alka.lv - Boosting the Common Voice Corpus for Low-Resource Languages", author = "Dargis, Roberts and Znotins, Arturs and Auzina, Ilze and Saulite, Baiba and Reinsone, Sanita and Dejus, Raivis and Klavinska, Antra and Gruzitis, Normunds", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.187", pages = "2080--2085", abstract = "Open speech corpora of substantial size are seldom available for less-spoken languages, and this was recently the case also for Latvian with its 1.5M native speakers. While there exist several closed Latvian speech corpora of 100+ hours, used to train competitive models for automatic speech recognition (ASR), there were only a few tiny open datasets available at the beginning of 2023, the 18-hour Latvian Common Voice 13.0 dataset being the largest one. In the result of a successful national crowdsourcing initiative, organised jointly by several institutions, the size and speaker diversity of the Latvian Common Voice 17.0 release have increased more than tenfold in less than a year. A successful follow-up initiative was also launched for Latgalian, which has been recognized as an endangered historic variant of Latvian with 150k speakers. The goal of these initiatives is not only to enlarge the datasets but also to make them more diverse in terms of speakers and accents, text genres and styles, intonations, grammar and lexicon. They have already become considerable language resources for both improving ASR and conducting linguistic research. Since we use the Mozilla Common Voice platform to record and validate speech samples, this paper focuses on (i) the selection of text snippets to enrich the language data and to stimulate various intonations, (ii) an indicative evaluation of the acquired corpus and the first ASR models fine-tuned on this data, (iii) our social campaigns to boost and maintain this initiative.", }

Open speech corpora of substantial size are seldom available for less-spoken languages, and this was recently the case also for Latvian with its 1.5M native speakers. While there exist several closed Latvian speech corpora of 100+ hours, used to train competitive models for automatic speech recognition (ASR), there were only a few tiny open datasets available at the beginning of 2023, the 18-hour Latvian Common Voice 13.0 dataset being the largest one. In the result of a successful national crowdsourcing initiative, organised jointly by several institutions, the size and speaker diversity of the Latvian Common Voice 17.0 release have increased more than tenfold in less than a year. A successful follow-up initiative was also launched for Latgalian, which has been recognized as an endangered historic variant of Latvian with 150k speakers. The goal of these initiatives is not only to enlarge the datasets but also to make them more diverse in terms of speakers and accents, text genres and styles, intonations, grammar and lexicon. They have already become considerable language resources for both improving ASR and conducting linguistic research. Since we use the Mozilla Common Voice platform to record and validate speech samples, this paper focuses on (i) the selection of text snippets to enrich the language data and to stimulate various intonations, (ii) an indicative evaluation of the acquired corpus and the first ASR models fine-tuned on this data, (iii) our social campaigns to boost and maintain this initiative.

BalsuTalka.lv - Boosting the Common Voice Corpus for Low-Resource Languages

lrec-main.187

Poster

https://aclanthology.org/2024.lrec-main.188.bib

https://aclanthology.org/2024.lrec-main.188/

@inproceedings{dong-etal-2024-bamboo, title = "{BAMBOO}: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models", author = "Dong, Zican and Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.188", pages = "2086--2099", abstract = "Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. Recently, multiple studies have committed to extending the context length and enhancing the long text modeling capabilities of LLMs. To comprehensively evaluate the long context ability of LLMs, we propose BAMBOO, a multi-task long context benchmark. BAMBOO has been designed with four principles: comprehensive capacity evaluation, avoidance of data contamination, accurate automatic evaluation, and different length levels. It consists of 10 datasets from 5 different long text understanding tasks, i.e., question answering, hallucination detection, text sorting, language modeling, and code completion, to cover various domains and core capacities of LLMs. We conduct experiments with five widely-used long-context models and further discuss five key questions for long text research. In the end, we discuss problems of current long-context models and point out future directions for enhancing long text modeling capacities. We release our data, prompts, and code at https://anonymous.4open.science/r/BAMBOO/.", }

Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. Recently, multiple studies have committed to extending the context length and enhancing the long text modeling capabilities of LLMs. To comprehensively evaluate the long context ability of LLMs, we propose BAMBOO, a multi-task long context benchmark. BAMBOO has been designed with four principles: comprehensive capacity evaluation, avoidance of data contamination, accurate automatic evaluation, and different length levels. It consists of 10 datasets from 5 different long text understanding tasks, i.e., question answering, hallucination detection, text sorting, language modeling, and code completion, to cover various domains and core capacities of LLMs. We conduct experiments with five widely-used long-context models and further discuss five key questions for long text research. In the end, we discuss problems of current long-context models and point out future directions for enhancing long text modeling capacities. We release our data, prompts, and code at https://anonymous.4open.science/r/BAMBOO/.

BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models

lrec-main.188

Poster

2309.13345

https://aclanthology.org/2024.lrec-main.189.bib

https://aclanthology.org/2024.lrec-main.189/

@inproceedings{wasi-etal-2024-banglaautokg, title = "{B}angla{A}uto{KG}: Automatic {B}angla Knowledge Graph Construction with Semantic Neural Graph Filtering", author = "Wasi, Azmine Toushik and Rafi, Taki Hasan and Islam, Raima and Chae, Dong-Kyu", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.189", pages = "2100--2106", abstract = "Knowledge Graphs (KGs) have proven essential in information processing and reasoning applications because they link related entities and give context-rich information, supporting efficient information retrieval and knowledge discovery; presenting information flow in a very effective manner. Despite being widely used globally, Bangla is relatively underrepresented in KGs due to a lack of comprehensive datasets, encoders, NER (named entity recognition) models, POS (part-of-speech) taggers, and lemmatizers, hindering efficient information processing and reasoning applications in the language. Addressing the KG scarcity in Bengali, we propose BanglaAutoKG, a pioneering framework that is able to automatically construct Bengali KGs from any Bangla text. We utilize multilingual LLMs to understand various languages and correlate entities and relations universally. By employing a translation dictionary to identify English equivalents and extracting word features from pre-trained BERT models, we construct the foundational KG. To reduce noise and align word embeddings with our goal, we employ graph-based polynomial filters. Lastly, we implement a GNN-based semantic filter, which elevates contextual understanding and trims unnecessary edges, culminating in the formation of the definitive KG. Empirical findings and case studies demonstrate the universal effectiveness of our model, capable of autonomously constructing semantically enriched KGs from any text. Data and code are available here: https://github.com/azminewasi/BanglaAutoKG", }

Knowledge Graphs (KGs) have proven essential in information processing and reasoning applications because they link related entities and give context-rich information, supporting efficient information retrieval and knowledge discovery; presenting information flow in a very effective manner. Despite being widely used globally, Bangla is relatively underrepresented in KGs due to a lack of comprehensive datasets, encoders, NER (named entity recognition) models, POS (part-of-speech) taggers, and lemmatizers, hindering efficient information processing and reasoning applications in the language. Addressing the KG scarcity in Bengali, we propose BanglaAutoKG, a pioneering framework that is able to automatically construct Bengali KGs from any Bangla text. We utilize multilingual LLMs to understand various languages and correlate entities and relations universally. By employing a translation dictionary to identify English equivalents and extracting word features from pre-trained BERT models, we construct the foundational KG. To reduce noise and align word embeddings with our goal, we employ graph-based polynomial filters. Lastly, we implement a GNN-based semantic filter, which elevates contextual understanding and trims unnecessary edges, culminating in the formation of the definitive KG. Empirical findings and case studies demonstrate the universal effectiveness of our model, capable of autonomously constructing semantically enriched KGs from any text. Data and code are available here: https://github.com/azminewasi/BanglaAutoKG

BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering

lrec-main.189

Poster

2404.03528

https://huggingface.co/papers/2404.03528

https://aclanthology.org/2024.lrec-main.190.bib

https://aclanthology.org/2024.lrec-main.190/

@inproceedings{kolos-etal-2024-ban, title = "{BAN}-{PL}: A {P}olish Dataset of Banned Harmful and Offensive Content from Wykop.pl Web Service", author = "Kolos, Anna and Okulska, Inez and G{\l}{\k{a}}bi{\'n}ska, Kinga and Karlinska, Agnieszka and Wisnios, Emilia and Ellerik, Pawe{\l} and Pra{\l}at, Andrzej", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.190", pages = "2107--2118", abstract = "Since the Internet is flooded with hate, it is one of the main tasks for NLP experts to master automated online content moderation. However, advancements in this field require improved access to publicly available accurate and non-synthetic datasets of social media content. For the Polish language, such resources are very limited. In this paper, we address this gap by presenting a new open dataset of offensive social media content for the Polish language. The dataset comprises content from Wykop.pl, a popular online service often referred to as the Polish Reddit, reported by users and banned in the internal moderation process. It contains a total of 691,662 posts and comments, evenly divided into two categories: harmful and neutral (non-harmful). The anonymized subset of the BAN-PL dataset consisting on 24,000 pieces (12,000 for each class), along with preprocessing scripts have been made publicly available. Furthermore the paper offers valuable insights into real-life content moderation processes and delves into an analysis of linguistic features and content characteristics of the dataset. Moreover, a comprehensive anonymization procedure has been meticulously described and applied. The prevalent biases encountered in similar datasets, including post-moderation and pre-selection biases, are also discussed.", }

Since the Internet is flooded with hate, it is one of the main tasks for NLP experts to master automated online content moderation. However, advancements in this field require improved access to publicly available accurate and non-synthetic datasets of social media content. For the Polish language, such resources are very limited. In this paper, we address this gap by presenting a new open dataset of offensive social media content for the Polish language. The dataset comprises content from Wykop.pl, a popular online service often referred to as the Polish Reddit, reported by users and banned in the internal moderation process. It contains a total of 691,662 posts and comments, evenly divided into two categories: harmful and neutral (non-harmful). The anonymized subset of the BAN-PL dataset consisting on 24,000 pieces (12,000 for each class), along with preprocessing scripts have been made publicly available. Furthermore the paper offers valuable insights into real-life content moderation processes and delves into an analysis of linguistic features and content characteristics of the dataset. Moreover, a comprehensive anonymization procedure has been meticulously described and applied. The prevalent biases encountered in similar datasets, including post-moderation and pre-selection biases, are also discussed.

BAN-PL: A Polish Dataset of Banned Harmful and Offensive Content from Wykop.pl Web Service

lrec-main.190

Poster

https://aclanthology.org/2024.lrec-main.191.bib

https://aclanthology.org/2024.lrec-main.191/

@inproceedings{zeng-etal-2024-barking, title = "{``}Barking up the Right Tree{''}, a {GAN}-Based Pun Generation Model through Semantic Pruning", author = "Zeng, JingJie and Yang, Liang and Kang, Jiahao and Diao, Yufeng and Yang, Zhihao and Lin, Hongfei", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.191", pages = "2119--2131", abstract = "In the realm of artificial intelligence and linguistics, the automatic generation of humor, particularly puns, remains a complex task. This paper introduces an innovative approach that employs a Generative Adversarial Network (GAN) and semantic pruning techniques to generate humorous puns. We initiate our process by identifying potential pun candidates via semantic pruning. This is followed by the use of contrastive learning to decode the unique characteristics of puns, emphasizing both correct and incorrect interpretations. The learned features from contrastive learning are utilized within our GAN model to better capture the semantic nuances of puns. Specifically, the generator exploits the pruned semantic tree to generate pun texts, while the discriminator evaluates the generated puns, ensuring both linguistic correctness and humor. Evaluation results highlight our model{'}s capacity to produce semantically coherent and humorous puns, demonstrating an enhancement over prior methods and approach human-level performance. This work contributes significantly to the field of computational humor, advancing the capabilities of automatic pun generation.", }

In the realm of artificial intelligence and linguistics, the automatic generation of humor, particularly puns, remains a complex task. This paper introduces an innovative approach that employs a Generative Adversarial Network (GAN) and semantic pruning techniques to generate humorous puns. We initiate our process by identifying potential pun candidates via semantic pruning. This is followed by the use of contrastive learning to decode the unique characteristics of puns, emphasizing both correct and incorrect interpretations. The learned features from contrastive learning are utilized within our GAN model to better capture the semantic nuances of puns. Specifically, the generator exploits the pruned semantic tree to generate pun texts, while the discriminator evaluates the generated puns, ensuring both linguistic correctness and humor. Evaluation results highlight our model{'}s capacity to produce semantically coherent and humorous puns, demonstrating an enhancement over prior methods and approach human-level performance. This work contributes significantly to the field of computational humor, advancing the capabilities of automatic pun generation.

“Barking up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning

lrec-main.191

Poster

https://aclanthology.org/2024.lrec-main.192.bib

https://aclanthology.org/2024.lrec-main.192/

@inproceedings{bengoetxea-etal-2024-basque, title = "{B}asque and {S}panish Counter Narrative Generation: Data Creation and Evaluation", author = "Bengoetxea, Jaione and Chung, Yi-Ling and Guerini, Marco and Agerri, Rodrigo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.192", pages = "2132--2141", abstract = "Counter Narratives (CNs) are non-negative textual responses to Hate Speech (HS) aiming at defusing online hatred and mitigating its spreading across media. Despite the recent increase in HS content posted online, research on automatic CN generation has been relatively scarce and predominantly focused on English. In this paper, we present CONAN-EUS, a new Basque and Spanish dataset for CN generation developed by means of Machine Translation (MT) and professional post-edition. Being a parallel corpus, also with respect to the original English CONAN, it allows to perform novel research on multilingual and crosslingual automatic generation of CNs. Our experiments on CN generation with mT5, a multilingual encoder-decoder model, shows that generation greatly benefits from training on post-edited data, as opposed to relying on silver MT data only. These results are confirmed by their correlation with a qualitative manual evaluation, demonstrating that manually revised training data remains crucial for the quality of the generated CNs. Furthermore, multilingual data augmentation improves results over monolingual settings for structurally similar languages such as English and Spanish, while being detrimental for Basque, a language isolate. Similar findings occur in zero-shot crosslingual evaluations, where model transfer (fine-tuning in English and generating in a different target language) outperforms fine-tuning mT5 on machine translated data for Spanish but not for Basque. This provides an interesting insight into the asymmetry in the multilinguality of generative models, a challenging topic which is still open to research. Data and code will be made publicly available upon publication.", }

Counter Narratives (CNs) are non-negative textual responses to Hate Speech (HS) aiming at defusing online hatred and mitigating its spreading across media. Despite the recent increase in HS content posted online, research on automatic CN generation has been relatively scarce and predominantly focused on English. In this paper, we present CONAN-EUS, a new Basque and Spanish dataset for CN generation developed by means of Machine Translation (MT) and professional post-edition. Being a parallel corpus, also with respect to the original English CONAN, it allows to perform novel research on multilingual and crosslingual automatic generation of CNs. Our experiments on CN generation with mT5, a multilingual encoder-decoder model, shows that generation greatly benefits from training on post-edited data, as opposed to relying on silver MT data only. These results are confirmed by their correlation with a qualitative manual evaluation, demonstrating that manually revised training data remains crucial for the quality of the generated CNs. Furthermore, multilingual data augmentation improves results over monolingual settings for structurally similar languages such as English and Spanish, while being detrimental for Basque, a language isolate. Similar findings occur in zero-shot crosslingual evaluations, where model transfer (fine-tuning in English and generating in a different target language) outperforms fine-tuning mT5 on machine translated data for Spanish but not for Basque. This provides an interesting insight into the asymmetry in the multilinguality of generative models, a challenging topic which is still open to research. Data and code will be made publicly available upon publication.

Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation

lrec-main.192

Poster

2403.09159

https://huggingface.co/papers/2403.09159

https://aclanthology.org/2024.lrec-main.193.bib

https://aclanthology.org/2024.lrec-main.193/

@inproceedings{armentano-oller-etal-2024-becoming, title = "Becoming a High-Resource Language in Speech: The {C}atalan Case in the Common Voice Corpus", author = "Armentano-Oller, Carme and Marimon, Montserrat and Villegas, Marta", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.193", pages = "2142--2148", abstract = "Collecting voice resources for speech recognition systems is a multifaceted challenge, involving legal, technical, and diversity considerations. However, it is crucial to ensure fair access to voice-driven technology across diverse linguistic backgrounds. We describe an ongoing effort to create an extensive, high-quality, publicly available voice dataset for future development of speech technologies in Catalan through the Mozilla Common Voice crowd-sourcing platform. We detail the specific approaches used to address the challenges faced in recruiting contributors and managing the collection, validation, and recording of sentences. This detailed overview can serve as a source of guidance for similar initiatives across other projects and linguistic contexts. The success of this project is evident in the latest corpus release, version 16.1, where Catalan ranks as the most prominent language in the corpus, both in terms of recorded hours and when considering validated hours. This establishes Catalan as a language with significant speech resources for language technology development and significantly raises its international visibility.", }

Collecting voice resources for speech recognition systems is a multifaceted challenge, involving legal, technical, and diversity considerations. However, it is crucial to ensure fair access to voice-driven technology across diverse linguistic backgrounds. We describe an ongoing effort to create an extensive, high-quality, publicly available voice dataset for future development of speech technologies in Catalan through the Mozilla Common Voice crowd-sourcing platform. We detail the specific approaches used to address the challenges faced in recruiting contributors and managing the collection, validation, and recording of sentences. This detailed overview can serve as a source of guidance for similar initiatives across other projects and linguistic contexts. The success of this project is evident in the latest corpus release, version 16.1, where Catalan ranks as the most prominent language in the corpus, both in terms of recorded hours and when considering validated hours. This establishes Catalan as a language with significant speech resources for language technology development and significantly raises its international visibility.

Becoming a High-Resource Language in Speech: The Catalan Case in the Common Voice Corpus

lrec-main.193

Poster

https://aclanthology.org/2024.lrec-main.194.bib

https://aclanthology.org/2024.lrec-main.194/

@inproceedings{wojtasik-etal-2024-beir, title = "{BEIR}-{PL}: Zero Shot Information Retrieval Benchmark for the {P}olish Language", author = "Wojtasik, Konrad and Wo{\l}owiec, Kacper and Shishkin, Vadim and Janz, Arkadiusz and Piasecki, Maciej", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.194", pages = "2149--2160", abstract = "The BEIR dataset is a large, heterogeneous benchmark for Information Retrieval (IR), garnering considerable attention within the research community. However, BEIR and analogous datasets are predominantly restricted to English language. Our objective is to establish extensive large-scale resources for IR in the Polish language, thereby advancing the research in this NLP area. In this work, inspired by mMARCO and Mr. TyDi datasets, we translated all accessible open IR datasets into Polish, and we introduced the BEIR-PL benchmark {--} a new benchmark which comprises 13 datasets, facilitating further development, training and evaluation of modern Polish language models for IR tasks. We executed an evaluation and comparison of numerous IR models on the newly introduced BEIR-PL benchmark. Furthermore, we publish pre-trained open IR models for Polish language, marking a pioneering development in this field. The BEIR-PL is included in MTEB Benchmark and also available with trained models at URL \url{https://huggingface.co/clarin-knext}.", }

The BEIR dataset is a large, heterogeneous benchmark for Information Retrieval (IR), garnering considerable attention within the research community. However, BEIR and analogous datasets are predominantly restricted to English language. Our objective is to establish extensive large-scale resources for IR in the Polish language, thereby advancing the research in this NLP area. In this work, inspired by mMARCO and Mr. TyDi datasets, we translated all accessible open IR datasets into Polish, and we introduced the BEIR-PL benchmark {--} a new benchmark which comprises 13 datasets, facilitating further development, training and evaluation of modern Polish language models for IR tasks. We executed an evaluation and comparison of numerous IR models on the newly introduced BEIR-PL benchmark. Furthermore, we publish pre-trained open IR models for Polish language, marking a pioneering development in this field. The BEIR-PL is included in MTEB Benchmark and also available with trained models at URL \url{https://huggingface.co/clarin-knext}.

BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language

lrec-main.194

Poster

2305.19840

https://huggingface.co/papers/2305.19840

https://aclanthology.org/2024.lrec-main.195.bib

https://aclanthology.org/2024.lrec-main.195/

@inproceedings{petruzzellis-etal-2024-benchmarking, title = "Benchmarking {GPT}-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies", author = "Petruzzellis, Flavio and Testolin, Alberto and Sperduti, Alessandro", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.195", pages = "2161--2177", abstract = "Large Language Models (LLMs) have revolutionized the field of Natural Language Processing thanks to their ability to reuse knowledge acquired on massive text corpora on a wide variety of downstream tasks, with minimal (if any) tuning steps. At the same time, it has been repeatedly shown that LLMs lack systematic generalization, which allows to extrapolate the learned statistical regularities outside the training distribution. In this work, we offer a systematic benchmarking of GPT-4, one of the most advanced LLMs available, on three algorithmic tasks characterized by the possibility to control the problem difficulty with two parameters. We compare the performance of GPT-4 with that of its predecessor (GPT-3.5) and with a variant of the Transformer-Encoder architecture recently introduced to solve similar tasks, the Neural Data Router. We find that the deployment of advanced prompting techniques allows GPT-4 to reach superior accuracy on all tasks, demonstrating that state-of-the-art LLMs constitute a very strong baseline also in challenging tasks that require systematic generalization.", }

Large Language Models (LLMs) have revolutionized the field of Natural Language Processing thanks to their ability to reuse knowledge acquired on massive text corpora on a wide variety of downstream tasks, with minimal (if any) tuning steps. At the same time, it has been repeatedly shown that LLMs lack systematic generalization, which allows to extrapolate the learned statistical regularities outside the training distribution. In this work, we offer a systematic benchmarking of GPT-4, one of the most advanced LLMs available, on three algorithmic tasks characterized by the possibility to control the problem difficulty with two parameters. We compare the performance of GPT-4 with that of its predecessor (GPT-3.5) and with a variant of the Transformer-Encoder architecture recently introduced to solve similar tasks, the Neural Data Router. We find that the deployment of advanced prompting techniques allows GPT-4 to reach superior accuracy on all tasks, demonstrating that state-of-the-art LLMs constitute a very strong baseline also in challenging tasks that require systematic generalization.

Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies

lrec-main.195

Poster

2402.17396

https://aclanthology.org/2024.lrec-main.196.bib

https://aclanthology.org/2024.lrec-main.196/

@inproceedings{sun-etal-2024-benchmarking, title = "Benchmarking Hallucination in Large Language Models Based on Unanswerable Math Word Problem", author = "Sun, YuHong and Yin, Zhangyue and Guo, Qipeng and Wu, Jiawen and Qiu, Xipeng and Zhao, Hui", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.196", pages = "2178--2188", abstract = "Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP). To support this approach, we innovatively develop a dataset called Unanswerable Math Word Problem (UMWP) which comprises 5200 questions across five categories. We developed an evaluation methodology combining text similarity and mathematical expression detection to determine whether LLM considers the question unanswerable. The results of extensive experiments conducted on 31 LLMs, including GPT-3, InstructGPT, LLaMA, and Claude, demonstrate that in-context learning and reinforcement learning with human feedback (RLHF) training significantly enhance the model{'}s ability to avoid hallucination. We show that utilizing MWP is a reliable and effective approach to assess hallucination. Our code and data are available at https://github.com/Yuki-Asuuna/UMWP.", }

Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP). To support this approach, we innovatively develop a dataset called Unanswerable Math Word Problem (UMWP) which comprises 5200 questions across five categories. We developed an evaluation methodology combining text similarity and mathematical expression detection to determine whether LLM considers the question unanswerable. The results of extensive experiments conducted on 31 LLMs, including GPT-3, InstructGPT, LLaMA, and Claude, demonstrate that in-context learning and reinforcement learning with human feedback (RLHF) training significantly enhance the model{'}s ability to avoid hallucination. We show that utilizing MWP is a reliable and effective approach to assess hallucination. Our code and data are available at https://github.com/Yuki-Asuuna/UMWP.

Benchmarking Hallucination in Large Language Models Based on Unanswerable Math Word Problem

lrec-main.196

Poster

2403.03558

https://aclanthology.org/2024.lrec-main.197.bib

https://aclanthology.org/2024.lrec-main.197/

@inproceedings{abaskohi-etal-2024-benchmarking, title = "Benchmarking Large Language Models for {P}ersian: A Preliminary Study Focusing on {C}hat{GPT}", author = "Abaskohi, Amirhossein and Baruni, Sara and Masoudi, Mostafa and Abbasi, Nesa and Babalou, Mohammad Hadi and Edalat, Ali and Kamahi, Sepehr and Mahdizadeh Sani, Samin and Naghavian, Nikoo and Namazifard, Danial and Sadeghi, Pouya and Yaghoobzadeh, Yadollah", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.197", pages = "2189--2203", abstract = "This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian language tasks. Our primary focus is on GPT-3.5-turbo, but we also include GPT-4 and OpenChat-3.5 to provide a more holistic evaluation. Our assessment encompasses a diverse set of tasks categorized into classic, reasoning, and knowledge-based domains. To enable a thorough comparison, we evaluate LLMs against existing task-specific fine-tuned models. Given the limited availability of Persian datasets for reasoning tasks, we introduce two new benchmarks: one based on elementary school math questions and another derived from the entrance exams for 7th and 10th grades. Our findings reveal that while LLMs, especially GPT-4, excel in tasks requiring reasoning abilities and a broad understanding of general knowledge, they often lag behind smaller pretrained models fine-tuned specifically for particular tasks. Additionally, we observe improved performance when test sets are translated to English before inputting them into GPT-3.5. These results highlight the significant potential for enhancing LLM performance in the Persian language. This is particularly noteworthy due to the unique attributes of Persian, including its distinct alphabet and writing styles. We have made our codes, prompts, and data available here: https://github.com/Ipouyall/Benchmarking{\_}ChatGPT{\_}for{\_}Persian.", }

This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian language tasks. Our primary focus is on GPT-3.5-turbo, but we also include GPT-4 and OpenChat-3.5 to provide a more holistic evaluation. Our assessment encompasses a diverse set of tasks categorized into classic, reasoning, and knowledge-based domains. To enable a thorough comparison, we evaluate LLMs against existing task-specific fine-tuned models. Given the limited availability of Persian datasets for reasoning tasks, we introduce two new benchmarks: one based on elementary school math questions and another derived from the entrance exams for 7th and 10th grades. Our findings reveal that while LLMs, especially GPT-4, excel in tasks requiring reasoning abilities and a broad understanding of general knowledge, they often lag behind smaller pretrained models fine-tuned specifically for particular tasks. Additionally, we observe improved performance when test sets are translated to English before inputting them into GPT-3.5. These results highlight the significant potential for enhancing LLM performance in the Persian language. This is particularly noteworthy due to the unique attributes of Persian, including its distinct alphabet and writing styles. We have made our codes, prompts, and data available here: https://github.com/Ipouyall/Benchmarking{\_}ChatGPT{\_}for{\_}Persian.

Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT

lrec-main.197

Poster

2404.02403

https://aclanthology.org/2024.lrec-main.198.bib

https://aclanthology.org/2024.lrec-main.198/

@inproceedings{song-xu-2024-benchmarking, title = "Benchmarking the Performance of Machine Translation Evaluation Metrics with {C}hinese Multiword Expressions", author = "Song, Huacheng and Xu, Hongzhi", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.198", pages = "2204--2216", abstract = "To investigate the impact of Multiword Expressions (MWEs) on the fine-grained performance of the state-of-the-art metrics for Machine Translation Evaluation (MTE), we conduct experiments on the WMT22 Metrics Shared Task dataset with a preliminary focus on the Chinese-to-English language pair. We further annotate 28 types of Chinese MWEs on the source texts and then examine the performance of 31 MTE metrics on groups of sentences containing different MWEs. We have 3 interesting findings: 1) Machine Translation (MT) systems tend to perform worse on most Chinese MWE categories, confirming the previous claim that MWEs are a bottleneck of MT; 2) automatic metrics tend to overrate the translation of sentences containing MWEs; 3) most neural-network-based metrics perform better than string-overlap-based metrics. It concludes that both MT systems and MTE metrics still suffer from MWEs, suggesting richer annotation of data to facilitate MWE-aware automatic MTE and MT.", }

To investigate the impact of Multiword Expressions (MWEs) on the fine-grained performance of the state-of-the-art metrics for Machine Translation Evaluation (MTE), we conduct experiments on the WMT22 Metrics Shared Task dataset with a preliminary focus on the Chinese-to-English language pair. We further annotate 28 types of Chinese MWEs on the source texts and then examine the performance of 31 MTE metrics on groups of sentences containing different MWEs. We have 3 interesting findings: 1) Machine Translation (MT) systems tend to perform worse on most Chinese MWE categories, confirming the previous claim that MWEs are a bottleneck of MT; 2) automatic metrics tend to overrate the translation of sentences containing MWEs; 3) most neural-network-based metrics perform better than string-overlap-based metrics. It concludes that both MT systems and MTE metrics still suffer from MWEs, suggesting richer annotation of data to facilitate MWE-aware automatic MTE and MT.

Benchmarking the Performance of Machine Translation Evaluation Metrics with Chinese Multiword Expressions

lrec-main.198

Poster

https://aclanthology.org/2024.lrec-main.199.bib

https://aclanthology.org/2024.lrec-main.199/

@inproceedings{vlantis-etal-2024-benchmarking, title = "Benchmarking the Simplification of {D}utch Municipal Text", author = "Vlantis, Daniel and Gornishka, Iva and Wang, Shuai", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.199", pages = "2217--2226", abstract = "Text simplification (TS) makes written information more accessible to all people, especially those with cognitive or language impairments. Despite much progress in TS due to advances in NLP technology, the bottleneck issue of lack of data for low-resource languages persists. Dutch is one of these languages that lack a monolingual simplification corpus. In this paper, we use English as a pivot language for the simplification of Dutch medical and municipal text. We experiment with augmenting training data and corpus choice for this pivot-based approach. We compare the results to a baseline and an end-to-end LLM approach using the GPT 3.5 Turbo model. Our evaluation shows that, while we can substantially improve the results of the pivot pipeline, the zero-shot end-to-end GPT-based simplification performs better on all metrics. Our work shows how an existing pivot-based pipeline can be improved for simplifying Dutch medical text. Moreover, we provide baselines for the comparison in the domain of Dutch municipal text and make our corresponding evaluation dataset publicly available.", }

Text simplification (TS) makes written information more accessible to all people, especially those with cognitive or language impairments. Despite much progress in TS due to advances in NLP technology, the bottleneck issue of lack of data for low-resource languages persists. Dutch is one of these languages that lack a monolingual simplification corpus. In this paper, we use English as a pivot language for the simplification of Dutch medical and municipal text. We experiment with augmenting training data and corpus choice for this pivot-based approach. We compare the results to a baseline and an end-to-end LLM approach using the GPT 3.5 Turbo model. Our evaluation shows that, while we can substantially improve the results of the pivot pipeline, the zero-shot end-to-end GPT-based simplification performs better on all metrics. Our work shows how an existing pivot-based pipeline can be improved for simplifying Dutch medical text. Moreover, we provide baselines for the comparison in the domain of Dutch municipal text and make our corresponding evaluation dataset publicly available.

Benchmarking the Simplification of Dutch Municipal Text

lrec-main.199

Poster

https://aclanthology.org/2024.lrec-main.200.bib

https://aclanthology.org/2024.lrec-main.200/

@inproceedings{ayman-etal-2024-bengalilcp, title = "{B}engali{LCP}: A Dataset for Lexical Complexity Prediction in the {B}engali Texts", author = "Ayman, Nabila and Hossain, Md. Akram and Aziz, Abdul and Faruqui, Rokan Uddin and Chy, Abu Nowshed", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.200", pages = "2227--2237", abstract = "Encountering intricate or ambiguous terms within a sentence produces distress for the reader during comprehension. Lexical Complexity Prediction (LCP) deals with predicting the complexity score of a word or a phrase considering its context. This task poses several challenges including ambiguity, context sensitivity, and subjectivity in perceiving complexity. Despite having 300 million native speakers and ranking as the seventh most spoken language in the world, Bengali falls behind in the research on lexical complexity when compared to other languages. To bridge this gap, we introduce the first annotated Bengali dataset, that assists in performing the task of LCP in this language. Besides, we propose a transformer-based deep neural approach with a pairwise multi-head attention mechanism and LSTM model to predict the lexical complexity of Bengali tokens. The outcomes demonstrate that the proposed neural approach outperformed the existing state-of-the-art models for the Bengali language.", }

Encountering intricate or ambiguous terms within a sentence produces distress for the reader during comprehension. Lexical Complexity Prediction (LCP) deals with predicting the complexity score of a word or a phrase considering its context. This task poses several challenges including ambiguity, context sensitivity, and subjectivity in perceiving complexity. Despite having 300 million native speakers and ranking as the seventh most spoken language in the world, Bengali falls behind in the research on lexical complexity when compared to other languages. To bridge this gap, we introduce the first annotated Bengali dataset, that assists in performing the task of LCP in this language. Besides, we propose a transformer-based deep neural approach with a pairwise multi-head attention mechanism and LSTM model to predict the lexical complexity of Bengali tokens. The outcomes demonstrate that the proposed neural approach outperformed the existing state-of-the-art models for the Bengali language.

BengaliLCP: A Dataset for Lexical Complexity Prediction in the Bengali Texts

lrec-main.200

Poster