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https://aclanthology.org/2024.lrec-main.1.bib | https://aclanthology.org/2024.lrec-main.1/ | @inproceedings{ma-etal-2024-3am,
title = "3{AM}: An Ambiguity-Aware Multi-Modal Machine Translation Dataset",
author = "Ma, Xinyu and
Liu, Xuebo and
Wong, Derek F. and
Rao, Jun and
Li, Bei and
Ding, Liang and
Chao, Lidia S. and
Tao, Dacheng and
Zhang, Min",
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.1",
pages = "1--13",
abstract = "Multimodal machine translation (MMT) is a challenging task that seeks to improve translation quality by incorporating visual information. However, recent studies have indicated that the visual information provided by existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. This issue presents a significant obstacle to the development of MMT research. This paper presents a novel solution to this issue by introducing 3AM, an ambiguity-aware MMT dataset comprising 26,000 parallel sentence pairs in English and Chinese, each with corresponding images. Our dataset is specifically designed to include more ambiguity and a greater variety of both captions and images than other MMT datasets. We utilize a word sense disambiguation model to select ambiguous data from vision-and-language datasets, resulting in a more challenging dataset. We further benchmark several state-of-the-art MMT models on our proposed dataset. Experimental results show that MMT models trained on our dataset exhibit a greater ability to exploit visual information than those trained on other MMT datasets. Our work provides a valuable resource for researchers in the field of multimodal learning and encourages further exploration in this area. The data, code and scripts are freely available at https://github.com/MaxyLee/3AM.",
}
| Multimodal machine translation (MMT) is a challenging task that seeks to improve translation quality by incorporating visual information. However, recent studies have indicated that the visual information provided by existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. This issue presents a significant obstacle to the development of MMT research. This paper presents a novel solution to this issue by introducing 3AM, an ambiguity-aware MMT dataset comprising 26,000 parallel sentence pairs in English and Chinese, each with corresponding images. Our dataset is specifically designed to include more ambiguity and a greater variety of both captions and images than other MMT datasets. We utilize a word sense disambiguation model to select ambiguous data from vision-and-language datasets, resulting in a more challenging dataset. We further benchmark several state-of-the-art MMT models on our proposed dataset. Experimental results show that MMT models trained on our dataset exhibit a greater ability to exploit visual information than those trained on other MMT datasets. Our work provides a valuable resource for researchers in the field of multimodal learning and encourages further exploration in this area. The data, code and scripts are freely available at https://github.com/MaxyLee/3AM. | [
"Ma, Xinyu",
"Liu, Xuebo",
"Wong, Derek F.",
"Rao, Jun",
"Li, Bei",
"Ding, Liang",
"Chao, Lidia S.",
"Tao, Dacheng",
"Zhang, Min"
] | 3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset | lrec-main.1 | Poster | 2404.18413 | [
"https://github.com/maxylee/3am"
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.2.bib | https://aclanthology.org/2024.lrec-main.2/ | @inproceedings{bannour-etal-2024-benchmark,
title = "A Benchmark Evaluation of Clinical Named Entity Recognition in {F}rench",
author = "Bannour, Nesrine and
Servan, Christophe and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Tannier, Xavier",
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.2",
pages = "14--21",
abstract = "Background: Transformer-based language models have shown strong performance on many Natural Language Processing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adapted to different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighter than modern Large Language Models (MLMs). Recently, several MLMs have been released for the biomedical domain in French, and experiments suggest that they outperform standard French counterparts. However, no systematic evaluation comparing all models on the same corpora is available. Objective: This paper presents an evaluation of masked language models for biomedical French on the task of clinical named entity recognition. Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them to standard French models CamemBERT, FlauBERT and FrAlBERT as well as multilingual mBERT using three publically available corpora for clinical named entity recognition in French. The evaluation set-up relies on gold-standard corpora as released by the corpus developers. Results: Results suggest that CamemBERT-bio outperforms DrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbon footprint. Conclusion: This is the first benchmark evaluation of biomedical masked language models for French clinical entity recognition that compares model performance consistently on nested entity recognition using metrics covering performance and environmental impact.",
}
| Background: Transformer-based language models have shown strong performance on many Natural Language Processing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adapted to different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighter than modern Large Language Models (MLMs). Recently, several MLMs have been released for the biomedical domain in French, and experiments suggest that they outperform standard French counterparts. However, no systematic evaluation comparing all models on the same corpora is available. Objective: This paper presents an evaluation of masked language models for biomedical French on the task of clinical named entity recognition. Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them to standard French models CamemBERT, FlauBERT and FrAlBERT as well as multilingual mBERT using three publically available corpora for clinical named entity recognition in French. The evaluation set-up relies on gold-standard corpora as released by the corpus developers. Results: Results suggest that CamemBERT-bio outperforms DrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbon footprint. Conclusion: This is the first benchmark evaluation of biomedical masked language models for French clinical entity recognition that compares model performance consistently on nested entity recognition using metrics covering performance and environmental impact. | [
"Bannour, Nesrine",
"Servan, Christophe",
"N{\\'e}v{\\'e}ol, Aur{\\'e}lie",
"Tannier, Xavier"
] | A Benchmark Evaluation of Clinical Named Entity Recognition in French | lrec-main.2 | Poster | 2403.19726 | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.3.bib | https://aclanthology.org/2024.lrec-main.3/ | @inproceedings{nevens-etal-2024-benchmark,
title = "A Benchmark for Recipe Understanding in Artificial Agents",
author = "Nevens, Jens and
de Haes, Robin and
Ringe, Rachel and
Pomarlan, Mihai and
Porzel, Robert and
Beuls, Katrien and
van Eecke, Paul",
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.3",
pages = "22--42",
abstract = "This paper introduces a novel benchmark that has been designed as a test bed for evaluating whether artificial agents are able to understand how to perform everyday activities, with a focus on the cooking domain. Understanding how to cook recipes is a highly challenging endeavour due to the underspecified and grounded nature of recipe texts, combined with the fact that recipe execution is a knowledge-intensive and precise activity. The benchmark comprises a corpus of recipes, a procedural semantic representation language of cooking actions, qualitative and quantitative kitchen simulators, and a standardised evaluation procedure. Concretely, the benchmark task consists in mapping a recipe formulated in natural language to a set of cooking actions that is precise enough to be executed in the simulated kitchen and yields the desired dish. To overcome the challenges inherent to recipe execution, this mapping process needs to incorporate reasoning over the recipe text, the state of the simulated kitchen environment, common-sense knowledge, knowledge of the cooking domain, and the action space of a virtual or robotic chef. This benchmark thereby addresses the growing interest in human-centric systems that combine natural language processing and situated reasoning to perform everyday activities.",
}
| This paper introduces a novel benchmark that has been designed as a test bed for evaluating whether artificial agents are able to understand how to perform everyday activities, with a focus on the cooking domain. Understanding how to cook recipes is a highly challenging endeavour due to the underspecified and grounded nature of recipe texts, combined with the fact that recipe execution is a knowledge-intensive and precise activity. The benchmark comprises a corpus of recipes, a procedural semantic representation language of cooking actions, qualitative and quantitative kitchen simulators, and a standardised evaluation procedure. Concretely, the benchmark task consists in mapping a recipe formulated in natural language to a set of cooking actions that is precise enough to be executed in the simulated kitchen and yields the desired dish. To overcome the challenges inherent to recipe execution, this mapping process needs to incorporate reasoning over the recipe text, the state of the simulated kitchen environment, common-sense knowledge, knowledge of the cooking domain, and the action space of a virtual or robotic chef. This benchmark thereby addresses the growing interest in human-centric systems that combine natural language processing and situated reasoning to perform everyday activities. | [
"Nevens, Jens",
"de Haes, Robin",
"Ringe, Rachel",
"Pomarlan, Mihai",
"Porzel, Robert",
"Beuls, Katrien",
"van Eecke, Paul"
] | A Benchmark for Recipe Understanding in Artificial Agents | lrec-main.3 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.4.bib | https://aclanthology.org/2024.lrec-main.4/ | @inproceedings{kim-etal-2024-able,
title = "{ABLE}: Agency-{B}e{L}iefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness",
author = "Kim, Michelle YoungJin and
Kim, Junghwan and
Johnson, Kristen",
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.4",
pages = "43--56",
abstract = "Natural Language Processing (NLP) models tend to inherit and amplify stereotypical biases present in their training data, leading to harmful societal consequences. Current efforts to rectify these biases typically revolve around making models oblivious to bias, which is at odds with the idea that humans require increased awareness to tackle these biases better. This prompts a fundamental research question: are bias-oblivious models the only viable solution to combat stereotypical biases? This paper answers this question by proposing the Agency-BeLiefs Embedding (ABLE) model, a novel approach that actively encodes stereotypical biases into the embedding space. ABLE draws upon social psychological theory to acquire and represent stereotypical biases in the form of agency and belief scores rather than directly representing stereotyped groups. Our experimental results showcase ABLE{'}s effectiveness in learning agency and belief stereotypes while preserving the language model{'}s proficiency. Furthermore, we underscore the practical significance of incorporating stereotypes within the ABLE model by demonstrating its utility in various downstream tasks. Our approach exemplifies the potential benefits of addressing bias through awareness, as opposed to the prevailing approach of mitigating bias through obliviousness.",
}
| Natural Language Processing (NLP) models tend to inherit and amplify stereotypical biases present in their training data, leading to harmful societal consequences. Current efforts to rectify these biases typically revolve around making models oblivious to bias, which is at odds with the idea that humans require increased awareness to tackle these biases better. This prompts a fundamental research question: are bias-oblivious models the only viable solution to combat stereotypical biases? This paper answers this question by proposing the Agency-BeLiefs Embedding (ABLE) model, a novel approach that actively encodes stereotypical biases into the embedding space. ABLE draws upon social psychological theory to acquire and represent stereotypical biases in the form of agency and belief scores rather than directly representing stereotyped groups. Our experimental results showcase ABLE{'}s effectiveness in learning agency and belief stereotypes while preserving the language model{'}s proficiency. Furthermore, we underscore the practical significance of incorporating stereotypes within the ABLE model by demonstrating its utility in various downstream tasks. Our approach exemplifies the potential benefits of addressing bias through awareness, as opposed to the prevailing approach of mitigating bias through obliviousness. | [
"Kim, Michelle YoungJin",
"Kim, Junghwan",
"Johnson, Kristen"
] | ABLE: Agency-BeLiefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness | lrec-main.4 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.5.bib | https://aclanthology.org/2024.lrec-main.5/ | @inproceedings{takahashi-etal-2024-abstractive,
title = "Abstractive Multi-Video Captioning: Benchmark Dataset Construction and Extensive Evaluation",
author = "Takahashi, Rikito and
Kiyomaru, Hirokazu and
Chu, Chenhui and
Kurohashi, Sadao",
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.5",
pages = "57--69",
abstract = "This paper introduces a new task, abstractive multi-video captioning, which focuses on abstracting multiple videos with natural language. Unlike conventional video captioning tasks generating a specific caption for a video, our task generates an abstract caption of the shared content in a video group containing multiple videos. To address our task, models must learn to understand each video in detail and have strong abstraction abilities to find commonalities among videos. We construct a benchmark dataset for abstractive multi-video captioning named AbstrActs. AbstrActs contains 13.5k video groups and corresponding abstract captions. As abstractive multi-video captioning models, we explore two approaches: end-to-end and cascade. For evaluation, we proposed a new metric, CocoA, which can evaluate the model performance based on the abstractness of the generated captions. In experiments, we report the impact of the way of combining multiple video features, the overall model architecture, and the number of input videos.",
}
| This paper introduces a new task, abstractive multi-video captioning, which focuses on abstracting multiple videos with natural language. Unlike conventional video captioning tasks generating a specific caption for a video, our task generates an abstract caption of the shared content in a video group containing multiple videos. To address our task, models must learn to understand each video in detail and have strong abstraction abilities to find commonalities among videos. We construct a benchmark dataset for abstractive multi-video captioning named AbstrActs. AbstrActs contains 13.5k video groups and corresponding abstract captions. As abstractive multi-video captioning models, we explore two approaches: end-to-end and cascade. For evaluation, we proposed a new metric, CocoA, which can evaluate the model performance based on the abstractness of the generated captions. In experiments, we report the impact of the way of combining multiple video features, the overall model architecture, and the number of input videos. | [
"Takahashi, Rikito",
"Kiyomaru, Hirokazu",
"Chu, Chenhui",
"Kurohashi, Sadao"
] | Abstractive Multi-Video Captioning: Benchmark Dataset Construction and Extensive Evaluation | lrec-main.5 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.6.bib | https://aclanthology.org/2024.lrec-main.6/ | @inproceedings{wu-etal-2024-abstract,
title = "Abstract-level Deductive Reasoning for Pre-trained Language Models",
author = "Wu, Xin and
Cai, Yi and
Leung, Ho-fung",
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.6",
pages = "70--76",
abstract = "Pre-trained Language Models have been shown to be able to emulate deductive reasoning in natural language. However, PLMs are easily affected by irrelevant information (e.g., entity) in instance-level proofs when learning deductive reasoning. To address this limitation, we propose an Abstract-level Deductive Reasoner (ADR). ADR is trained to predict the abstract reasoning proof of each sample, which guides PLMs to learn general reasoning patterns rather than instance-level knowledge. Experimental results demonstrate that ADR significantly reduces the impact of PLMs learning instance-level knowledge (over 70{\%}).",
}
| Pre-trained Language Models have been shown to be able to emulate deductive reasoning in natural language. However, PLMs are easily affected by irrelevant information (e.g., entity) in instance-level proofs when learning deductive reasoning. To address this limitation, we propose an Abstract-level Deductive Reasoner (ADR). ADR is trained to predict the abstract reasoning proof of each sample, which guides PLMs to learn general reasoning patterns rather than instance-level knowledge. Experimental results demonstrate that ADR significantly reduces the impact of PLMs learning instance-level knowledge (over 70{\%}). | [
"Wu, Xin",
"Cai, Yi",
"Leung, Ho-fung"
] | Abstract-level Deductive Reasoning for Pre-trained Language Models | lrec-main.6 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.7.bib | https://aclanthology.org/2024.lrec-main.7/ | @inproceedings{kasai-etal-2024-call,
title = "A Call for Clarity in Beam Search: How It Works and When It Stops",
author = "Kasai, Jungo and
Sakaguchi, Keisuke and
Le Bras, Ronan and
Radev, Dragomir and
Choi, Yejin and
Smith, Noah A.",
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.7",
pages = "77--90",
abstract = "Text generation with beam search has proven successful in a wide range of applications. We point out that, though largely overlooked in the literature, the commonly-used implementation of beam decoding (e.g., Hugging Face Transformers and fairseq) uses a first come, first served heuristic: it keeps a set of already completed sequences over time steps and stops when the size of this set reaches the beam size. Based on this finding, we introduce a patience factor, a simple modification to this beam decoding implementation, that generalizes the stopping criterion and provides flexibility to the depth of search. Empirical results demonstrate that adjusting this patience factor improves decoding performance of strong pretrained models on news text summarization and machine translation over diverse language pairs, with a negligible inference slowdown. Our approach only modifies one line of code and can be thus readily incorporated in any implementation. Further, we find that different versions of beam decoding result in large performance differences in summarization, demonstrating the need for clarity in specifying the beam search implementation in research work. Our code will be available upon publication.",
}
| Text generation with beam search has proven successful in a wide range of applications. We point out that, though largely overlooked in the literature, the commonly-used implementation of beam decoding (e.g., Hugging Face Transformers and fairseq) uses a first come, first served heuristic: it keeps a set of already completed sequences over time steps and stops when the size of this set reaches the beam size. Based on this finding, we introduce a patience factor, a simple modification to this beam decoding implementation, that generalizes the stopping criterion and provides flexibility to the depth of search. Empirical results demonstrate that adjusting this patience factor improves decoding performance of strong pretrained models on news text summarization and machine translation over diverse language pairs, with a negligible inference slowdown. Our approach only modifies one line of code and can be thus readily incorporated in any implementation. Further, we find that different versions of beam decoding result in large performance differences in summarization, demonstrating the need for clarity in specifying the beam search implementation in research work. Our code will be available upon publication. | [
"Kasai, Jungo",
"Sakaguchi, Keisuke",
"Le Bras, Ronan",
"Radev, Dragomir",
"Choi, Yejin",
"Smith, Noah A."
] | A Call for Clarity in Beam Search: How It Works and When It Stops | lrec-main.7 | Poster | 2204.05424 | [
"https://github.com/jungokasai/beam_with_patience"
] | https://huggingface.co/papers/2204.05424 | 0 | 0 | 0 | 6 | 1 | [] | [] | [
"ashhadahsan/whisperX",
"katospiegel/amanu"
] |
https://aclanthology.org/2024.lrec-main.8.bib | https://aclanthology.org/2024.lrec-main.8/ | @inproceedings{odijk-kroon-2024-canonical,
title = "A Canonical Form for Flexible Multiword Expressions",
author = "Odijk, Jan and
Kroon, Martin",
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.8",
pages = "91--101",
abstract = "This paper proposes a canonical form for Multiword Expressions (MWEs), in particular for the Dutch language. The canonical form can be enriched with all kinds of annotations that can be used to describe the properties of the MWE and its components. It also introduces the DUCAME (DUtch CAnonical Multiword Expressions) lexical resource with more than 11k MWEs in canonical form. DUCAME is used in MWE-Finder to automatically generate queries for searching for flexible MWEs in large text corpora.",
}
| This paper proposes a canonical form for Multiword Expressions (MWEs), in particular for the Dutch language. The canonical form can be enriched with all kinds of annotations that can be used to describe the properties of the MWE and its components. It also introduces the DUCAME (DUtch CAnonical Multiword Expressions) lexical resource with more than 11k MWEs in canonical form. DUCAME is used in MWE-Finder to automatically generate queries for searching for flexible MWEs in large text corpora. | [
"Odijk, Jan",
"Kroon, Martin"
] | A Canonical Form for Flexible Multiword Expressions | lrec-main.8 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.9.bib | https://aclanthology.org/2024.lrec-main.9/ | @inproceedings{yu-etal-2024-cause,
title = "A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation",
author = "Yu, Jifan and
Zhang, Xiaohan and
Xu, Yifan and
Lei, Xuanyu and
Yao, Zijun and
Zhang, Jing and
Hou, Lei and
Li, Juanzi",
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.9",
pages = "102--112",
abstract = "Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. However, they are still plagued by the {\textless}b{\textgreater}hallucination{\textless}/b{\textgreater} problem, causing unpredictable factual errors in the generated responses. Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination. Following the idea of getting high-quality knowledge, a few efforts have achieved pretty good performance on this issue. As some inevitable knowledge noises may also lead to hallucinations, it is emergent to investigate the reason and future directions for building noise-tolerant methods in KGD tasks. In this paper, we analyze the causal story behind this problem with counterfactual reasoning methods. Based on the causal effect analysis, we propose a possible solution for alleviating the hallucination in KGD by exploiting the dialogue-knowledge interaction. Experimental results of our example implementation show that this method can reduce hallucination without disrupting other dialogue performance, while keeping adaptive to different generation models. We hope our efforts can support and call for more attention to developing lightweight techniques towards robust and trusty dialogue systems.",
}
| Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. However, they are still plagued by the {\textless}b{\textgreater}hallucination{\textless}/b{\textgreater} problem, causing unpredictable factual errors in the generated responses. Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination. Following the idea of getting high-quality knowledge, a few efforts have achieved pretty good performance on this issue. As some inevitable knowledge noises may also lead to hallucinations, it is emergent to investigate the reason and future directions for building noise-tolerant methods in KGD tasks. In this paper, we analyze the causal story behind this problem with counterfactual reasoning methods. Based on the causal effect analysis, we propose a possible solution for alleviating the hallucination in KGD by exploiting the dialogue-knowledge interaction. Experimental results of our example implementation show that this method can reduce hallucination without disrupting other dialogue performance, while keeping adaptive to different generation models. We hope our efforts can support and call for more attention to developing lightweight techniques towards robust and trusty dialogue systems. | [
"Yu, Jifan",
"Zhang, Xiaohan",
"Xu, Yifan",
"Lei, Xuanyu",
"Yao, Zijun",
"Zhang, Jing",
"Hou, Lei",
"Li, Juanzi"
] | A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation | lrec-main.9 | Poster | 2404.03491 | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.10.bib | https://aclanthology.org/2024.lrec-main.10/ | @inproceedings{foley-etal-2024-access,
title = "Access Control Framework for Language Collections",
author = "Foley, Ben and
Sefton, Peter and
Musgrave, Simon and
Sacal Bonequi, Moises",
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.10",
pages = "113--121",
abstract = "This paper introduces the licence-based access control framework developed by the Language Data Commons of Australia (LDaCA) for a range of language collections, with examples given of implementation for significant Indigenous and Australian English collections. Language collections may be curated for many reasons, such as documentation for language revival, for research, security or commercial purposes. Some language collections are created with the intention of being {``}Open Access{''}; publicly available with no restriction. Other collections require that access be limited to individuals or groups of people, either at the collection level or at the level of individual items, such as a recording. To facilitate access, while respecting the intended access conditions for a collection, or collection items, some form of user identification and authorisation process is typically required. The access control framework described in this paper is based upon descriptions of access conditions in easy-to-read licences which are stored alongside data files in the collections; and is implemented using identity-based authentication and authorisation systems where required. The framework accommodates accessibility needs from unrestricted to extremely limited access, is dynamic, and able to be modified in response to changes in access needs. Storing licences with the data is a significant development in separating language data and access requirements from access infrastructure.",
}
| This paper introduces the licence-based access control framework developed by the Language Data Commons of Australia (LDaCA) for a range of language collections, with examples given of implementation for significant Indigenous and Australian English collections. Language collections may be curated for many reasons, such as documentation for language revival, for research, security or commercial purposes. Some language collections are created with the intention of being {``}Open Access{''}; publicly available with no restriction. Other collections require that access be limited to individuals or groups of people, either at the collection level or at the level of individual items, such as a recording. To facilitate access, while respecting the intended access conditions for a collection, or collection items, some form of user identification and authorisation process is typically required. The access control framework described in this paper is based upon descriptions of access conditions in easy-to-read licences which are stored alongside data files in the collections; and is implemented using identity-based authentication and authorisation systems where required. The framework accommodates accessibility needs from unrestricted to extremely limited access, is dynamic, and able to be modified in response to changes in access needs. Storing licences with the data is a significant development in separating language data and access requirements from access infrastructure. | [
"Foley, Ben",
"Sefton, Peter",
"Musgrave, Simon",
"Sacal Bonequi, Moises"
] | Access Control Framework for Language Collections | lrec-main.10 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.11.bib | https://aclanthology.org/2024.lrec-main.11/ | @inproceedings{niu-etal-2024-challenge,
title = "A Challenge Dataset and Effective Models for Conversational Stance Detection",
author = "Niu, Fuqiang and
Yang, Min and
Li, Ang and
Zhang, Baoquan and
Peng, Xiaojiang and
Zhang, Bowen",
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.11",
pages = "122--132",
abstract = "Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in authentic social media interactions. This constraint arises primarily due to the scarcity of datasets that authentically replicate real social media contexts, hindering the research progress of conversational stance detection. In this paper, we introduce a new multi-turn conversation stance detection dataset (called \textbf{MT-CSD}), which encompasses multiple targets for conversational stance detection. To derive stances from this challenging dataset, we propose a global-local attention network (\textbf{GLAN}) to address both long and short-range dependencies inherent in conversational data. Notably, even state-of-the-art stance detection methods, exemplified by GLAN, exhibit an accuracy of only 50.47{\%}, highlighting the persistent challenges in conversational stance detection. Furthermore, our MT-CSD dataset serves as a valuable resource to catalyze advancements in cross-domain stance detection, where a classifier is adapted from a different yet related target. We believe that MT-CSD will contribute to advancing real-world applications of stance detection research. Our source code, data, and models are available at \url{https://github.com/nfq729/MT-CSD}.",
}
| Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in authentic social media interactions. This constraint arises primarily due to the scarcity of datasets that authentically replicate real social media contexts, hindering the research progress of conversational stance detection. In this paper, we introduce a new multi-turn conversation stance detection dataset (called \textbf{MT-CSD}), which encompasses multiple targets for conversational stance detection. To derive stances from this challenging dataset, we propose a global-local attention network (\textbf{GLAN}) to address both long and short-range dependencies inherent in conversational data. Notably, even state-of-the-art stance detection methods, exemplified by GLAN, exhibit an accuracy of only 50.47{\%}, highlighting the persistent challenges in conversational stance detection. Furthermore, our MT-CSD dataset serves as a valuable resource to catalyze advancements in cross-domain stance detection, where a classifier is adapted from a different yet related target. We believe that MT-CSD will contribute to advancing real-world applications of stance detection research. Our source code, data, and models are available at \url{https://github.com/nfq729/MT-CSD}. | [
"Niu, Fuqiang",
"Yang, Min",
"Li, Ang",
"Zhang, Baoquan",
"Peng, Xiaojiang",
"Zhang, Bowen"
] | A Challenge Dataset and Effective Models for Conversational Stance Detection | lrec-main.11 | Poster | 2403.11145 | [
"https://github.com/nfq729/mt-csd"
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.12.bib | https://aclanthology.org/2024.lrec-main.12/ | @inproceedings{laskina-etal-2024-closer,
title = "A Closer Look at Clustering Bilingual Comparable Corpora",
author = "Laskina, Anna and
Gaussier, Eric and
Calvary, Gaelle",
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.12",
pages = "133--142",
abstract = "We study in this paper the problem of clustering comparable corpora, building upon the observation that different types of clusters can be present in such corpora: monolingual clusters comprising documents in a single language, and bilingual or multilingual clusters comprising documents written in different languages. Based on a state-of-the-art deep variant of Kmeans, we propose new clustering models fully adapted to comparable corpora and illustrate their behavior on several bilingual collections (in English, French, German and Russian) created from Wikipedia.",
}
| We study in this paper the problem of clustering comparable corpora, building upon the observation that different types of clusters can be present in such corpora: monolingual clusters comprising documents in a single language, and bilingual or multilingual clusters comprising documents written in different languages. Based on a state-of-the-art deep variant of Kmeans, we propose new clustering models fully adapted to comparable corpora and illustrate their behavior on several bilingual collections (in English, French, German and Russian) created from Wikipedia. | [
"Laskina, Anna",
"Gaussier, Eric",
"Calvary, Gaelle"
] | A Closer Look at Clustering Bilingual Comparable Corpora | lrec-main.12 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.13.bib | https://aclanthology.org/2024.lrec-main.13/ | @inproceedings{lee-parde-2024-acnempathize,
title = "{A}cn{E}mpathize: A Dataset for Understanding Empathy in Dermatology Conversations",
author = "Lee, Gyeongeun and
Parde, Natalie",
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.13",
pages = "143--153",
abstract = "Empathy is critical for effective communication and mental health support, and in many online health communities people anonymously engage in conversations to seek and provide empathetic support. The ability to automatically recognize and detect empathy contributes to the understanding of human emotions expressed in text, therefore advancing natural language understanding across various domains. Existing empathy and mental health-related corpora focus on broader contexts and lack domain specificity, but similarly to other tasks (e.g., learning distinct patterns associated with COVID-19 versus skin allergies in clinical notes), observing empathy within different domains is crucial to providing tailored support. To address this need, we introduce AcnEmpathize, a dataset that captures empathy expressed in acne-related discussions from forum posts focused on its emotional and psychological effects. We find that transformer-based models trained on our dataset demonstrate excellent performance at empathy classification. Our dataset is publicly released to facilitate analysis of domain-specific empathy in online conversations and advance research in this challenging and intriguing domain.",
}
| Empathy is critical for effective communication and mental health support, and in many online health communities people anonymously engage in conversations to seek and provide empathetic support. The ability to automatically recognize and detect empathy contributes to the understanding of human emotions expressed in text, therefore advancing natural language understanding across various domains. Existing empathy and mental health-related corpora focus on broader contexts and lack domain specificity, but similarly to other tasks (e.g., learning distinct patterns associated with COVID-19 versus skin allergies in clinical notes), observing empathy within different domains is crucial to providing tailored support. To address this need, we introduce AcnEmpathize, a dataset that captures empathy expressed in acne-related discussions from forum posts focused on its emotional and psychological effects. We find that transformer-based models trained on our dataset demonstrate excellent performance at empathy classification. Our dataset is publicly released to facilitate analysis of domain-specific empathy in online conversations and advance research in this challenging and intriguing domain. | [
"Lee, Gyeongeun",
"Parde, Natalie"
] | AcnEmpathize: A Dataset for Understanding Empathy in Dermatology Conversations | lrec-main.13 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.14.bib | https://aclanthology.org/2024.lrec-main.14/ | @inproceedings{ward-marco-2024-collection,
title = "A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances",
author = "Ward, Nigel and
Marco, Divette",
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.14",
pages = "154--163",
abstract = "Automatic measures of similarity between sentences or utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity. To enable the training of such measures, we developed the first collection of human judgments of pragmatic similarity between utterance pairs. 9 judges listened to 220 utterance pairs, each consisting of an utterance extracted from a recorded dialog and a re-enactment of that utterance under various conditions designed to create various degrees of similarity. Each pair was rated on a continuous scale. The average inter-judge correlation was 0.45. We make this data available at https://github.com/divettemarco/PragSim .",
}
| Automatic measures of similarity between sentences or utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity. To enable the training of such measures, we developed the first collection of human judgments of pragmatic similarity between utterance pairs. 9 judges listened to 220 utterance pairs, each consisting of an utterance extracted from a recorded dialog and a re-enactment of that utterance under various conditions designed to create various degrees of similarity. Each pair was rated on a continuous scale. The average inter-judge correlation was 0.45. We make this data available at https://github.com/divettemarco/PragSim . | [
"Ward, Nigel",
"Marco, Divette"
] | A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances | lrec-main.14 | Poster | 2403.14808 | [
"https://github.com/divettemarco/pragsim"
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.15.bib | https://aclanthology.org/2024.lrec-main.15/ | @inproceedings{fernandes-etal-2024-community,
title = "A Community-Driven Data-to-Text Platform for Football Match Summaries",
author = "Fernandes, Pedro and
Nunes, S{\'e}rgio and
Santos, Lu{\'\i}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.15",
pages = "164--173",
abstract = "Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system enhances the visibility of lower-tier matches, traditionally accessible only through data tables. Prosebot uses a template-based Natural Language Generation (NLG) module to generate initial drafts, which are subsequently refined by the reading community. Comprehensive evaluations, encompassing both human-mediated and automated assessments, were conducted to assess the system{'}s efficacy. Analysis of the community-edited texts reveals that significant segments of the initial automated drafts are retained, suggesting their high quality and acceptance by the collaborators. Preliminary surveys conducted among platform users highlight a predominantly positive reception within the community.",
}
| Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system enhances the visibility of lower-tier matches, traditionally accessible only through data tables. Prosebot uses a template-based Natural Language Generation (NLG) module to generate initial drafts, which are subsequently refined by the reading community. Comprehensive evaluations, encompassing both human-mediated and automated assessments, were conducted to assess the system{'}s efficacy. Analysis of the community-edited texts reveals that significant segments of the initial automated drafts are retained, suggesting their high quality and acceptance by the collaborators. Preliminary surveys conducted among platform users highlight a predominantly positive reception within the community. | [
"Fern",
"es, Pedro",
"Nunes, S{\\'e}rgio",
"Santos, Lu{\\'\\i}s"
] | A Community-Driven Data-to-Text Platform for Football Match Summaries | lrec-main.15 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.16.bib | https://aclanthology.org/2024.lrec-main.16/ | @inproceedings{meisenbacher-etal-2024-comparative,
title = "A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking the Privacy-Utility Trade-off",
author = "Meisenbacher, Stephen and
Nandakumar, Nihildev and
Klymenko, Alexandra and
Matthes, Florian",
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.16",
pages = "174--185",
abstract = "The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in established NLP outlets. In particular, the adaptation of Differential Privacy for use in NLP tasks has first focused on the *word-level*, where calibrated noise is added to word embedding vectors to achieve {``}noisy{''} representations. To this end, several implementations have appeared in the literature, each presenting an alternative method of achieving word-level Differential Privacy. Although each of these includes its own evaluation, no comparative analysis has been performed to investigate the performance of such methods relative to each other. In this work, we conduct such an analysis, comparing seven different algorithms on two NLP tasks with varying hyperparameters, including the *epsilon* parameter, or privacy budget. In addition, we provide an in-depth analysis of the results with a focus on the privacy-utility trade-off, as well as open-source our implementation code for further reproduction. As a result of our analysis, we give insight into the benefits and challenges of word-level Differential Privacy, and accordingly, we suggest concrete steps forward for the research field.",
}
| The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in established NLP outlets. In particular, the adaptation of Differential Privacy for use in NLP tasks has first focused on the *word-level*, where calibrated noise is added to word embedding vectors to achieve {``}noisy{''} representations. To this end, several implementations have appeared in the literature, each presenting an alternative method of achieving word-level Differential Privacy. Although each of these includes its own evaluation, no comparative analysis has been performed to investigate the performance of such methods relative to each other. In this work, we conduct such an analysis, comparing seven different algorithms on two NLP tasks with varying hyperparameters, including the *epsilon* parameter, or privacy budget. In addition, we provide an in-depth analysis of the results with a focus on the privacy-utility trade-off, as well as open-source our implementation code for further reproduction. As a result of our analysis, we give insight into the benefits and challenges of word-level Differential Privacy, and accordingly, we suggest concrete steps forward for the research field. | [
"Meisenbacher, Stephen",
"N",
"akumar, Nihildev",
"Klymenko, Alex",
"ra",
"Matthes, Florian"
] | A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking the Privacy-Utility Trade-off | lrec-main.16 | Poster | 2404.03324 | [
"https://github.com/sjmeis/mldp"
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.17.bib | https://aclanthology.org/2024.lrec-main.17/ | @inproceedings{zhao-etal-2024-comparative,
title = "A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation",
author = "Zhao, Yachao and
Wang, Bo and
Wang, Yan and
Zhao, Dongming and
Jin, Xiaojia and
Zhang, Jijun and
He, Ruifang and
Hou, Yuexian",
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.17",
pages = "186--198",
abstract = "While extensive work has examined the explicit and implicit biases in large language models (LLMs), little research explores the relation between these two types of biases. This paper presents a comparative study of the explicit and implicit biases in LLMs grounded in social psychology. Social psychology distinguishes between explicit and implicit biases by whether the bias can be self-recognized by individuals. Aligning with this conceptualization, we propose a self-evaluation-based two-stage measurement of explicit and implicit biases within LLMs. First, the LLM is prompted to automatically fill templates with social targets to measure implicit bias toward these targets, where the bias is less likely to be self-recognized by the LLM. Then, the LLM is prompted to self-evaluate the templates filled by itself to measure explicit bias toward the same targets, where the bias is more likely to be self-recognized by the LLM. Experiments conducted on state-of-the-art LLMs reveal human-like inconsistency between explicit and implicit occupational gender biases. This work bridges a critical gap where prior studies concentrate solely on either explicit or implicit bias. We advocate that future work highlight the relation between explicit and implicit biases in LLMs.",
}
| While extensive work has examined the explicit and implicit biases in large language models (LLMs), little research explores the relation between these two types of biases. This paper presents a comparative study of the explicit and implicit biases in LLMs grounded in social psychology. Social psychology distinguishes between explicit and implicit biases by whether the bias can be self-recognized by individuals. Aligning with this conceptualization, we propose a self-evaluation-based two-stage measurement of explicit and implicit biases within LLMs. First, the LLM is prompted to automatically fill templates with social targets to measure implicit bias toward these targets, where the bias is less likely to be self-recognized by the LLM. Then, the LLM is prompted to self-evaluate the templates filled by itself to measure explicit bias toward the same targets, where the bias is more likely to be self-recognized by the LLM. Experiments conducted on state-of-the-art LLMs reveal human-like inconsistency between explicit and implicit occupational gender biases. This work bridges a critical gap where prior studies concentrate solely on either explicit or implicit bias. We advocate that future work highlight the relation between explicit and implicit biases in LLMs. | [
"Zhao, Yachao",
"Wang, Bo",
"Wang, Yan",
"Zhao, Dongming",
"Jin, Xiaojia",
"Zhang, Jijun",
"He, Ruifang",
"Hou, Yuexian"
] | A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation | lrec-main.17 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.18.bib | https://aclanthology.org/2024.lrec-main.18/ | @inproceedings{caporusso-etal-2024-computational,
title = "A Computational Analysis of the Dehumanisation of Migrants from Syria and {U}kraine in {S}lovene News Media",
author = "Caporusso, Jaya and
Hoogland, Damar and
Brglez, Mojca and
Koloski, Boshko and
Purver, Matthew and
Pollak, Senja",
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.18",
pages = "199--210",
abstract = "Dehumanisation involves the perception and/or treatment of a social group{'}s members as less than human. This phenomenon is rarely addressed with computational linguistic techniques. We adapt a recently proposed approach for English, making it easier to transfer to other languages and to evaluate, introducing a new sentiment resource, the use of zero-shot cross-lingual valence and arousal detection, and a new method for statistical significance testing. We then apply it to study attitudes to migration expressed in Slovene newspapers, to examine changes in the Slovene discourse on migration between the 2015-16 migration crisis following the war in Syria and the 2022-23 period following the war in Ukraine. We find that while this discourse became more negative and more intense over time, it is less dehumanising when specifically addressing Ukrainian migrants compared to others.",
}
| Dehumanisation involves the perception and/or treatment of a social group{'}s members as less than human. This phenomenon is rarely addressed with computational linguistic techniques. We adapt a recently proposed approach for English, making it easier to transfer to other languages and to evaluate, introducing a new sentiment resource, the use of zero-shot cross-lingual valence and arousal detection, and a new method for statistical significance testing. We then apply it to study attitudes to migration expressed in Slovene newspapers, to examine changes in the Slovene discourse on migration between the 2015-16 migration crisis following the war in Syria and the 2022-23 period following the war in Ukraine. We find that while this discourse became more negative and more intense over time, it is less dehumanising when specifically addressing Ukrainian migrants compared to others. | [
"Caporusso, Jaya",
"Hoogl",
", Damar",
"Brglez, Mojca",
"Koloski, Boshko",
"Purver, Matthew",
"Pollak, Senja"
] | A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media | lrec-main.18 | Poster | 2404.07036 | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.19.bib | https://aclanthology.org/2024.lrec-main.19/ | @inproceedings{nagata-etal-2024-computational,
title = "A Computational Approach to Quantifying Grammaticization of {E}nglish Deverbal Prepositions",
author = "Nagata, Ryo and
Kawasaki, Yoshifumi and
Otani, Naoki and
Takamura, Hiroya",
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.19",
pages = "211--220",
abstract = "This paper explores grammaticization of deverbal prepositions by a computational approach based on corpus data. Deverbal prepositions are words or phrases that are derived from a verb and that behave as a preposition such as {``}regarding{''} and {``}according to{''}. Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. This paper augments them by methods for measuring the degree of grammaticization of deverbal prepositions based on non-contextualized or contextualized word vectors. Experiments show that the methods correlate well with human judgements (as high as 0.69 in Spearman{'}s rank correlation coefficient). Using the best-performing method, this paper further shows that the methods support previous findings in linguistics including (i) Deverbal prepositions are marginal in terms of prepositionality; and (ii) The process where verbs are grammaticized into prepositions is gradual. As a pilot study, it also conducts a diachronic analysis of grammaticization of deverbal preposition.",
}
| This paper explores grammaticization of deverbal prepositions by a computational approach based on corpus data. Deverbal prepositions are words or phrases that are derived from a verb and that behave as a preposition such as {``}regarding{''} and {``}according to{''}. Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. This paper augments them by methods for measuring the degree of grammaticization of deverbal prepositions based on non-contextualized or contextualized word vectors. Experiments show that the methods correlate well with human judgements (as high as 0.69 in Spearman{'}s rank correlation coefficient). Using the best-performing method, this paper further shows that the methods support previous findings in linguistics including (i) Deverbal prepositions are marginal in terms of prepositionality; and (ii) The process where verbs are grammaticized into prepositions is gradual. As a pilot study, it also conducts a diachronic analysis of grammaticization of deverbal preposition. | [
"Nagata, Ryo",
"Kawasaki, Yoshifumi",
"Otani, Naoki",
"Takamura, Hiroya"
] | A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions | lrec-main.19 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.20.bib | https://aclanthology.org/2024.lrec-main.20/ | @inproceedings{paikens-etal-2024-computational,
title = "A Computational Model of {L}atvian Morphology",
author = "Paikens, Peteris and
Pretkalni{\c{n}}a, Lauma and
Rituma, Laura",
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.20",
pages = "221--232",
abstract = "In this paper we describe a computational model of Latvian morphology that provides a formal structure for Latvian word form inflection and has been implemented in software for generation, analysis and lemmatization of Latvian word forms. The work was motivated by the need for a NLP inflection model that can cover all the complexity of Latvian language and explicitly enumerate and handle the many exceptions to the general Latvian inflection principles. This is an evolution of earlier work, extending the initial proof of concept model to properly cover Latvian language. We provide a set of morphological paradigms that differ from current linguistic tradition, a set of systematic stem changes and combine it with an extensive lexicon that includes paradigm information and structured morphological attributes for 118 000 lexemes. This model has been applied on both dictionary and corpora data, demonstrating that it provides a good coverage for modern Latvian literary language. We also consider that there is a good potential to extend this also to the related Latgalian language.",
}
| In this paper we describe a computational model of Latvian morphology that provides a formal structure for Latvian word form inflection and has been implemented in software for generation, analysis and lemmatization of Latvian word forms. The work was motivated by the need for a NLP inflection model that can cover all the complexity of Latvian language and explicitly enumerate and handle the many exceptions to the general Latvian inflection principles. This is an evolution of earlier work, extending the initial proof of concept model to properly cover Latvian language. We provide a set of morphological paradigms that differ from current linguistic tradition, a set of systematic stem changes and combine it with an extensive lexicon that includes paradigm information and structured morphological attributes for 118 000 lexemes. This model has been applied on both dictionary and corpora data, demonstrating that it provides a good coverage for modern Latvian literary language. We also consider that there is a good potential to extend this also to the related Latgalian language. | [
"Paikens, Peteris",
"Pretkalni{\\c{n}}a, Lauma",
"Rituma, Laura"
] | A Computational Model of Latvian Morphology | lrec-main.20 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.21.bib | https://aclanthology.org/2024.lrec-main.21/ | @inproceedings{gerlach-etal-2024-concept,
title = "A Concept Based Approach for Translation of Medical Dialogues into Pictographs",
author = "Gerlach, Johanna and
Bouillon, Pierrette and
Mutal, Jonathan and
Spechbach, Herv{\'e}",
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.21",
pages = "233--242",
abstract = "Pictographs have been found to improve patient comprehension of medical information or instructions. However, tools to produce pictograph representations from natural language are still scarce. In this contribution we describe a system that automatically translates French speech into pictographs to enable diagnostic interviews in emergency settings, thereby providing a tool to overcome the language barrier or provide support in Augmentative and Alternative Communication (AAC) contexts. Our approach is based on a semantic gloss that serves as pivot between spontaneous language and pictographs, with medical concepts represented using the UMLS ontology. In this study we evaluate different available pre-trained models fine-tuned on artificial data to translate French into this semantic gloss. On unseen data collected in real settings, consisting of questions and instructions by physicians, the best model achieves an F0.5 score of 86.7. A complementary human evaluation of the semantic glosses differing from the reference shows that 71{\%} of these would be usable to transmit the intended meaning. Finally, a human evaluation of the pictograph sequences derived from the gloss reveals very few additions, omissions or order issues ({\textless}3{\%}), suggesting that the gloss as designed is well suited as a pivot for translation into pictographs.",
}
| Pictographs have been found to improve patient comprehension of medical information or instructions. However, tools to produce pictograph representations from natural language are still scarce. In this contribution we describe a system that automatically translates French speech into pictographs to enable diagnostic interviews in emergency settings, thereby providing a tool to overcome the language barrier or provide support in Augmentative and Alternative Communication (AAC) contexts. Our approach is based on a semantic gloss that serves as pivot between spontaneous language and pictographs, with medical concepts represented using the UMLS ontology. In this study we evaluate different available pre-trained models fine-tuned on artificial data to translate French into this semantic gloss. On unseen data collected in real settings, consisting of questions and instructions by physicians, the best model achieves an F0.5 score of 86.7. A complementary human evaluation of the semantic glosses differing from the reference shows that 71{\%} of these would be usable to transmit the intended meaning. Finally, a human evaluation of the pictograph sequences derived from the gloss reveals very few additions, omissions or order issues ({\textless}3{\%}), suggesting that the gloss as designed is well suited as a pivot for translation into pictographs. | [
"Gerlach, Johanna",
"Bouillon, Pierrette",
"Mutal, Jonathan",
"Spechbach, Herv{\\'e}"
] | A Concept Based Approach for Translation of Medical Dialogues into Pictographs | lrec-main.21 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.22.bib | https://aclanthology.org/2024.lrec-main.22/ | @inproceedings{bonial-tayyar-madabushi-2024-construction,
title = "A Construction Grammar Corpus of Varying Schematicity: A Dataset for the Evaluation of Abstractions in Language Models",
author = "Bonial, Claire and
Tayyar Madabushi, Harish",
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.22",
pages = "243--255",
abstract = "Large Language Models (LLMs) have been developed without a theoretical framework, yet we posit that evaluating and improving LLMs will benefit from the development of theoretical frameworks that enable comparison of the structures of human language and the model of language built up by LLMs through the processing of text. In service of this goal, we develop the Construction Grammar Schematicity ({``}CoGS{''}) corpus of 10 distinct English constructions, where the constructions vary with respect to schematicity, or in other words the level to which constructional slots require specific, fixed lexical items, or can be filled with a variety of elements that fulfill a particular semantic role of the slot. Our corpus constructions are carefully curated to range from substantive, frozen constructions (e.g., Let-alone) to entirely schematic constructions (e.g., Resultative). The corpus was collected to allow us to probe LLMs for constructional information at varying levels of abstraction. We present our own probing experiments using this corpus, which clearly demonstrate that even the largest LLMs are limited to more substantive constructions and do not exhibit recognition of the similarity of purely schematic constructions. We publicly release our dataset, prompts, and associated model responses.",
}
| Large Language Models (LLMs) have been developed without a theoretical framework, yet we posit that evaluating and improving LLMs will benefit from the development of theoretical frameworks that enable comparison of the structures of human language and the model of language built up by LLMs through the processing of text. In service of this goal, we develop the Construction Grammar Schematicity ({``}CoGS{''}) corpus of 10 distinct English constructions, where the constructions vary with respect to schematicity, or in other words the level to which constructional slots require specific, fixed lexical items, or can be filled with a variety of elements that fulfill a particular semantic role of the slot. Our corpus constructions are carefully curated to range from substantive, frozen constructions (e.g., Let-alone) to entirely schematic constructions (e.g., Resultative). The corpus was collected to allow us to probe LLMs for constructional information at varying levels of abstraction. We present our own probing experiments using this corpus, which clearly demonstrate that even the largest LLMs are limited to more substantive constructions and do not exhibit recognition of the similarity of purely schematic constructions. We publicly release our dataset, prompts, and associated model responses. | [
"Bonial, Claire",
"Tayyar Madabushi, Harish"
] | A Construction Grammar Corpus of Varying Schematicity: A Dataset for the Evaluation of Abstractions in Language Models | lrec-main.22 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.23.bib | https://aclanthology.org/2024.lrec-main.23/ | @inproceedings{porada-etal-2024-controlled,
title = "A Controlled Reevaluation of Coreference Resolution Models",
author = "Porada, Ian and
Zou, Xiyuan and
Cheung, Jackie Chi Kit",
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.23",
pages = "256--263",
abstract = "All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR model over another is due to the choice of language model or other factors, such as the task-specific architecture, is difficult or impossible to determine due to lack of a standardized experimental setup. To resolve this ambiguity, we systematically evaluate five CR models and control for certain design decisions including the pretrained language model used by each. When controlling for language model size, encoder-based CR models outperform more recent decoder-based models in terms of both accuracy and inference speed. Surprisingly, among encoder-based CR models, more recent models are not always more accurate, and the oldest CR model that we test generalizes the best to out-of-domain textual genres. We conclude that controlling for the choice of language model reduces most, but not all, of the increase in F1 score reported in the past five years.",
}
| All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR model over another is due to the choice of language model or other factors, such as the task-specific architecture, is difficult or impossible to determine due to lack of a standardized experimental setup. To resolve this ambiguity, we systematically evaluate five CR models and control for certain design decisions including the pretrained language model used by each. When controlling for language model size, encoder-based CR models outperform more recent decoder-based models in terms of both accuracy and inference speed. Surprisingly, among encoder-based CR models, more recent models are not always more accurate, and the oldest CR model that we test generalizes the best to out-of-domain textual genres. We conclude that controlling for the choice of language model reduces most, but not all, of the increase in F1 score reported in the past five years. | [
"Porada, Ian",
"Zou, Xiyuan",
"Cheung, Jackie Chi Kit"
] | A Controlled Reevaluation of Coreference Resolution Models | lrec-main.23 | Poster | 2404.00727 | [
"https://github.com/ianporada/coref-reeval"
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.24.bib | https://aclanthology.org/2024.lrec-main.24/ | @inproceedings{li-etal-2024-corpus,
title = "A Corpus and Method for {C}hinese Named Entity Recognition in Manufacturing",
author = "Li, Ruiting and
Wang, Peiyan and
Wang, Libang and
Yang, Danqingxin and
Cai, Dongfeng",
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.24",
pages = "264--272",
abstract = "Manufacturing specifications are documents entailing different techniques, processes, and components involved in manufacturing. There is a growing demand for named entity recognition (NER) resources and techniques for manufacturing-specific named entities, with the development of smart manufacturing. In this paper, we introduce a corpus of Chinese manufacturing specifications, named MS-NERC, including 4,424 sentences and 16,383 entities. We also propose an entity recognizer named Trainable State Transducer (TST), which is initialized with a finite state transducer describing the morphological patterns of entities. It can directly recognize entities based on prior morphological knowledge without training. Experimental results show that TST achieves an overall 82.05{\%} F1 score for morphological-specific entities in zero-shot. TST can be improved through training, the result of which outperforms neural methods in few-shot and rich-resource. We believe that our corpus and model will be valuable resources for NER research not only in manufacturing but also in other low-resource domains.",
}
| Manufacturing specifications are documents entailing different techniques, processes, and components involved in manufacturing. There is a growing demand for named entity recognition (NER) resources and techniques for manufacturing-specific named entities, with the development of smart manufacturing. In this paper, we introduce a corpus of Chinese manufacturing specifications, named MS-NERC, including 4,424 sentences and 16,383 entities. We also propose an entity recognizer named Trainable State Transducer (TST), which is initialized with a finite state transducer describing the morphological patterns of entities. It can directly recognize entities based on prior morphological knowledge without training. Experimental results show that TST achieves an overall 82.05{\%} F1 score for morphological-specific entities in zero-shot. TST can be improved through training, the result of which outperforms neural methods in few-shot and rich-resource. We believe that our corpus and model will be valuable resources for NER research not only in manufacturing but also in other low-resource domains. | [
"Li, Ruiting",
"Wang, Peiyan",
"Wang, Libang",
"Yang, Danqingxin",
"Cai, Dongfeng"
] | A Corpus and Method for Chinese Named Entity Recognition in Manufacturing | lrec-main.24 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.25.bib | https://aclanthology.org/2024.lrec-main.25/ | @inproceedings{antici-etal-2024-corpus,
title = "A Corpus for Sentence-Level Subjectivity Detection on {E}nglish News Articles",
author = "Antici, Francesco and
Ruggeri, Federico and
Galassi, Andrea and
Korre, Katerina and
Muti, Arianna and
Bardi, Alessandra and
Fedotova, Alice and
Barr{\'o}n-Cede{\~n}o, 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.25",
pages = "273--285",
abstract = "We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. We evaluate state-of-the-art multilingual transformer-based models on the task in mono-, multi-, and cross-language settings. For this purpose, we re-annotate an existing Italian corpus. We observe that models trained in the multilingual setting achieve the best performance on the task.",
}
| We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English and across other languages without relying on language-specific tools, such as lexicons or machine translation. We evaluate state-of-the-art multilingual transformer-based models on the task in mono-, multi-, and cross-language settings. For this purpose, we re-annotate an existing Italian corpus. We observe that models trained in the multilingual setting achieve the best performance on the task. | [
"Antici, Francesco",
"Ruggeri, Federico",
"Galassi, Andrea",
"Korre, Katerina",
"Muti, Arianna",
"Bardi, Aless",
"ra",
"Fedotova, Alice",
"Barr{\\'o}n-Cede{\\~n}o, Alberto"
] | A Corpus for Sentence-Level Subjectivity Detection on English News Articles | lrec-main.25 | Poster | 2305.18034 | [
"https://github.com/lt-nlp-lab-unibo/newssd-eng"
] | https://huggingface.co/papers/2305.18034 | 0 | 0 | 0 | 8 | 1 | [] | [
"tasksource/subjectivity"
] | [] |
https://aclanthology.org/2024.lrec-main.26.bib | https://aclanthology.org/2024.lrec-main.26/ | @inproceedings{otto-etal-2024-corpus,
title = "A Corpus of {G}erman {A}bstract {M}eaning {R}epresentation ({D}e{AMR})",
author = "Otto, Christoph and
Groschwitz, Jonas and
Koller, Alexander and
Yang, Xiulin and
Donatelli, Lucia",
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.26",
pages = "286--292",
abstract = "We present the first comprehensive set of guidelines for German Abstract Meaning Representation (Deutsche AMR, DeAMR) along with an annotated corpus of 400 DeAMR. Taking English AMR (EnAMR) as our starting point, we propose significant adaptations to faithfully represent the structure and semantics of German, focusing particularly on verb frames, compound words, and modality. We validate our annotation through inter-annotator agreement and further evaluate our corpus with a comparison of structural divergences between EnAMR and DeAMR on parallel sentences, replicating previous work that finds both cases of cross-lingual structural alignment and cases of meaningful linguistic divergence. Finally, we fine-tune state-of-the-art multi-lingual and cross-lingual AMR parsers on our corpus and find that, while our small corpus is insufficient to produce quality output, there is a need to continue develop and evaluate against gold non-English AMR data.",
}
| We present the first comprehensive set of guidelines for German Abstract Meaning Representation (Deutsche AMR, DeAMR) along with an annotated corpus of 400 DeAMR. Taking English AMR (EnAMR) as our starting point, we propose significant adaptations to faithfully represent the structure and semantics of German, focusing particularly on verb frames, compound words, and modality. We validate our annotation through inter-annotator agreement and further evaluate our corpus with a comparison of structural divergences between EnAMR and DeAMR on parallel sentences, replicating previous work that finds both cases of cross-lingual structural alignment and cases of meaningful linguistic divergence. Finally, we fine-tune state-of-the-art multi-lingual and cross-lingual AMR parsers on our corpus and find that, while our small corpus is insufficient to produce quality output, there is a need to continue develop and evaluate against gold non-English AMR data. | [
"Otto, Christoph",
"Groschwitz, Jonas",
"Koller, Alex",
"er",
"Yang, Xiulin",
"Donatelli, Lucia"
] | A Corpus of German Abstract Meaning Representation (DeAMR) | lrec-main.26 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.27.bib | https://aclanthology.org/2024.lrec-main.27/ | @inproceedings{coulange-etal-2024-corpus,
title = "A Corpus of Spontaneous {L}2 {E}nglish Speech for Real-situation Speaking Assessment",
author = "Coulange, Sylvain and
Fries, Marie-H{\'e}l{\`e}ne and
Masperi, Monica and
Rossato, Solange",
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.27",
pages = "293--297",
abstract = "When assessing second language proficiency (L2), evaluation of spontaneous speech performance is crucial. This paper presents a corpus of spontaneous L2 English speech, focusing on the speech performance of B1 and B2 proficiency speakers. Two hundred and sixty university students were recorded during a speaking task as part of a French national certificate in English. This task entailed a 10-minute role-play among 2 or 3 candidates, arguing about a controversial topic, in order to reach a negotiated compromise. Each student{'}s performance was evaluated by two experts, categorizing them into B2, B1 or below B1 speaking proficiency levels. Automatic diarization, transcription, and alignment at the word level were performed on the recorded conversations, in order to analyse lexical stress realisation in polysyllabic plain words of B1 and B2 proficiency students. Results showed that only 35.4{\%} of the 6,350 targeted words had stress detected on the expected syllable, revealing a common stress shift to the final syllable. Besides a substantial inter-speaker variability (0{\%} to 68.4{\%}), B2 speakers demonstrated a slightly higher stress accuracy (36{\%}) compared to B1 speakers (29.6{\%}). Those with accurate stress placement utilized F0 and intensity to make syllable prominence, while speakers with lower accuracy tended to lengthen words on their last syllables, with minimal changes in other dimensions.",
}
| When assessing second language proficiency (L2), evaluation of spontaneous speech performance is crucial. This paper presents a corpus of spontaneous L2 English speech, focusing on the speech performance of B1 and B2 proficiency speakers. Two hundred and sixty university students were recorded during a speaking task as part of a French national certificate in English. This task entailed a 10-minute role-play among 2 or 3 candidates, arguing about a controversial topic, in order to reach a negotiated compromise. Each student{'}s performance was evaluated by two experts, categorizing them into B2, B1 or below B1 speaking proficiency levels. Automatic diarization, transcription, and alignment at the word level were performed on the recorded conversations, in order to analyse lexical stress realisation in polysyllabic plain words of B1 and B2 proficiency students. Results showed that only 35.4{\%} of the 6,350 targeted words had stress detected on the expected syllable, revealing a common stress shift to the final syllable. Besides a substantial inter-speaker variability (0{\%} to 68.4{\%}), B2 speakers demonstrated a slightly higher stress accuracy (36{\%}) compared to B1 speakers (29.6{\%}). Those with accurate stress placement utilized F0 and intensity to make syllable prominence, while speakers with lower accuracy tended to lengthen words on their last syllables, with minimal changes in other dimensions. | [
"Coulange, Sylvain",
"Fries, Marie-H{\\'e}l{\\`e}ne",
"Masperi, Monica",
"Rossato, Solange"
] | A Corpus of Spontaneous L2 English Speech for Real-situation Speaking Assessment | lrec-main.27 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.28.bib | https://aclanthology.org/2024.lrec-main.28/ | @inproceedings{tomar-etal-2024-action,
title = "Action and Reaction Go Hand in Hand! a Multi-modal Dialogue Act Aided Sarcasm Identification",
author = "Tomar, Mohit Singh and
Saha, Tulika and
Tiwari, Abhisek and
Saha, Sriparna",
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.28",
pages = "298--309",
abstract = "Sarcasm primarily involves saying something but {``}meaning the opposite{''} or {``}meaning something completely different{''} in order to convey a particular tone or mood. In both the above cases, the {``}meaning{''} is reflected by the communicative intention of the speaker, known as dialogue acts. In this paper, we seek to investigate a novel phenomenon of analyzing sarcasm in the context of dialogue acts with the hypothesis that the latter helps to understand the former better. Toward this aim, we extend the multi-modal MUStARD dataset to enclose dialogue acts for each dialogue. To demonstrate the utility of our hypothesis, we develop a dialogue act-aided multi-modal transformer network for sarcasm identification (MM-SARDAC), leveraging interrelation between these tasks. In addition, we introduce an order-infused, multi-modal infusion mechanism into our proposed model, which allows for a more intuitive combined modality representation by selectively focusing on relevant modalities in an ordered manner. Extensive empirical results indicate that dialogue act-aided sarcasm identification achieved better performance compared to performing sarcasm identification alone. The dataset and code are available at https://github.com/mohit2b/MM-SARDAC.",
}
| Sarcasm primarily involves saying something but {``}meaning the opposite{''} or {``}meaning something completely different{''} in order to convey a particular tone or mood. In both the above cases, the {``}meaning{''} is reflected by the communicative intention of the speaker, known as dialogue acts. In this paper, we seek to investigate a novel phenomenon of analyzing sarcasm in the context of dialogue acts with the hypothesis that the latter helps to understand the former better. Toward this aim, we extend the multi-modal MUStARD dataset to enclose dialogue acts for each dialogue. To demonstrate the utility of our hypothesis, we develop a dialogue act-aided multi-modal transformer network for sarcasm identification (MM-SARDAC), leveraging interrelation between these tasks. In addition, we introduce an order-infused, multi-modal infusion mechanism into our proposed model, which allows for a more intuitive combined modality representation by selectively focusing on relevant modalities in an ordered manner. Extensive empirical results indicate that dialogue act-aided sarcasm identification achieved better performance compared to performing sarcasm identification alone. The dataset and code are available at https://github.com/mohit2b/MM-SARDAC. | [
"Tomar, Mohit Singh",
"Saha, Tulika",
"Tiwari, Abhisek",
"Saha, Sriparna"
] | Action and Reaction Go Hand in Hand! a Multi-modal Dialogue Act Aided Sarcasm Identification | lrec-main.28 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.29.bib | https://aclanthology.org/2024.lrec-main.29/ | @inproceedings{yu-etal-2024-action,
title = "Action-Concentrated Embedding Framework: This Is Your Captain Sign-tokening",
author = "Yu, Hyunwook and
Shin, Suhyeon and
Heo, Junku and
Shin, Hyuntaek and
Kim, Hyosu and
Kim, Mucheol",
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.29",
pages = "310--320",
abstract = "Sign language is the primary communication medium for people who are deaf or have hearing loss. However, given the divergent range of sensory abilities of these individuals, there is a communication gap that needs to be addressed. In this paper, we present action-concentrated embedding (ACE), which is a novel sign token embedding framework. Additionally, to provide a more structured foundation for sign language analysis, we introduce a dedicated notation system tailored for sign language that endeavors to encapsulate the nuanced gestures and movements that are integral with sign communication. The proposed ACE approach tracks a signer{'}s actions based on human posture estimation. Tokenizing these actions and capturing the token embedding using a short-time Fourier transform encapsulates the time-based behavioral changes. Hence, ACE offers input embedding to translate sign language into natural language sentences. When tested against a disaster sign language dataset using automated machine translation measures, ACE notably surpasses prior research in terms of translation capabilities, improving the performance by up to 5.79{\%} for BLEU-4 and 5.46{\%} for ROUGE-L metric.",
}
| Sign language is the primary communication medium for people who are deaf or have hearing loss. However, given the divergent range of sensory abilities of these individuals, there is a communication gap that needs to be addressed. In this paper, we present action-concentrated embedding (ACE), which is a novel sign token embedding framework. Additionally, to provide a more structured foundation for sign language analysis, we introduce a dedicated notation system tailored for sign language that endeavors to encapsulate the nuanced gestures and movements that are integral with sign communication. The proposed ACE approach tracks a signer{'}s actions based on human posture estimation. Tokenizing these actions and capturing the token embedding using a short-time Fourier transform encapsulates the time-based behavioral changes. Hence, ACE offers input embedding to translate sign language into natural language sentences. When tested against a disaster sign language dataset using automated machine translation measures, ACE notably surpasses prior research in terms of translation capabilities, improving the performance by up to 5.79{\%} for BLEU-4 and 5.46{\%} for ROUGE-L metric. | [
"Yu, Hyunwook",
"Shin, Suhyeon",
"Heo, Junku",
"Shin, Hyuntaek",
"Kim, Hyosu",
"Kim, Mucheol"
] | Action-Concentrated Embedding Framework: This Is Your Captain Sign-tokening | lrec-main.29 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.30.bib | https://aclanthology.org/2024.lrec-main.30/ | @inproceedings{vacareanu-etal-2024-active,
title = "Active Learning Design Choices for {NER} with Transformers",
author = "Vacareanu, Robert and
Noriega-Atala, Enrique and
Hahn-Powell, Gus and
Valenzuela-Escarcega, Marco A. and
Surdeanu, Mihai",
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.30",
pages = "321--334",
abstract = "We explore multiple important choices that have not been analyzed in conjunction regarding active learning for token classification using transformer networks. These choices are: (i) how to select what to annotate, (ii) decide whether to annotate entire sentences or smaller sentence fragments, (iii) how to train with incomplete annotations at token-level, and (iv) how to select the initial seed dataset. We explore whether annotating at sub-sentence level can translate to an improved downstream performance by considering two different sub-sentence annotation strategies: (i) entity-level, and (ii) token-level. These approaches result in some sentences being only partially annotated. To address this issue, we introduce and evaluate multiple strategies to deal with partially-annotated sentences during the training process. We show that annotating at the sub-sentence level achieves comparable or better performance than sentence-level annotations with a smaller number of annotated tokens. We then explore the extent to which the performance gap remains once accounting for the annotation time and found that both annotation schemes perform similarly.",
}
| We explore multiple important choices that have not been analyzed in conjunction regarding active learning for token classification using transformer networks. These choices are: (i) how to select what to annotate, (ii) decide whether to annotate entire sentences or smaller sentence fragments, (iii) how to train with incomplete annotations at token-level, and (iv) how to select the initial seed dataset. We explore whether annotating at sub-sentence level can translate to an improved downstream performance by considering two different sub-sentence annotation strategies: (i) entity-level, and (ii) token-level. These approaches result in some sentences being only partially annotated. To address this issue, we introduce and evaluate multiple strategies to deal with partially-annotated sentences during the training process. We show that annotating at the sub-sentence level achieves comparable or better performance than sentence-level annotations with a smaller number of annotated tokens. We then explore the extent to which the performance gap remains once accounting for the annotation time and found that both annotation schemes perform similarly. | [
"Vacareanu, Robert",
"Noriega-Atala, Enrique",
"Hahn-Powell, Gus",
"Valenzuela-Escarcega, Marco A.",
"Surdeanu, Mihai"
] | Active Learning Design Choices for NER with Transformers | lrec-main.30 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.31.bib | https://aclanthology.org/2024.lrec-main.31/ | @inproceedings{palomar-giner-etal-2024-curated,
title = "A {CURATE}d {CAT}alog: Rethinking the Extraction of Pretraining Corpora for Mid-Resourced Languages",
author = "Palomar-Giner, Jorge and
Saiz, Jose Javier and
Espu{\~n}a, Ferran and
Mina, Mario and
Da Dalt, Severino and
Llop, Joan and
Ostendorff, Malte and
Ortiz Suarez, Pedro and
Rehm, Georg and
Gonzalez-Agirre, Aitor 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.31",
pages = "335--349",
abstract = "We present and describe two language resources in this paper: CATalog 1.0, the largest text corpus in Catalan to date, and CURATE (Corpus Utility for RAting TExt), a modular, parallelizable pipeline used for processing and scoring documents based on text quality that we have optimised to run in High Performance Cluster (HPC) environments. In the coming sections we describe our data preprocessing pipeline at length; traditional pipelines usually implement a set of binary filters such that a given document is either in or out. In our experience with Catalan, in lower-resource settings it is more practical to instead assign a document a soft score to allow for more flexible decision-making. We describe how the document score is calculated and highlight its interpretability by showing that it is significantly correlated with human judgements as obtained from a comparative judgement experiment. We additionally describe the different subcorpora that make up CATalog 1.0.",
}
| We present and describe two language resources in this paper: CATalog 1.0, the largest text corpus in Catalan to date, and CURATE (Corpus Utility for RAting TExt), a modular, parallelizable pipeline used for processing and scoring documents based on text quality that we have optimised to run in High Performance Cluster (HPC) environments. In the coming sections we describe our data preprocessing pipeline at length; traditional pipelines usually implement a set of binary filters such that a given document is either in or out. In our experience with Catalan, in lower-resource settings it is more practical to instead assign a document a soft score to allow for more flexible decision-making. We describe how the document score is calculated and highlight its interpretability by showing that it is significantly correlated with human judgements as obtained from a comparative judgement experiment. We additionally describe the different subcorpora that make up CATalog 1.0. | [
"Palomar-Giner, Jorge",
"Saiz, Jose Javier",
"Espu{\\~n}a, Ferran",
"Mina, Mario",
"Da Dalt, Severino",
"Llop, Joan",
"Ostendorff, Malte",
"Ortiz Suarez, Pedro",
"Rehm, Georg",
"Gonzalez-Agirre, Aitor",
"Villegas, Marta"
] | A CURATEd CATalog: Rethinking the Extraction of Pretraining Corpora for Mid-Resourced Languages | lrec-main.31 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.32.bib | https://aclanthology.org/2024.lrec-main.32/ | @inproceedings{braga-etal-2024-adakron,
title = "{A}da{K}ron: An Adapter-based Parameter Efficient Model Tuning with Kronecker Product",
author = "Braga, Marco and
Raganato, Alessandro and
Pasi, Gabriella",
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.32",
pages = "350--357",
abstract = "The fine-tuning paradigm has been widely adopted to train neural models tailored for specific tasks. However, the recent upsurge of Large Language Models (LLMs), characterized by billions of parameters, has introduced profound computational challenges to the fine-tuning process. This has fueled intensive research on Parameter-Efficient Fine-Tuning (PEFT) techniques, usually involving the training of a selective subset of the original model parameters. One of the most used approaches is Adapters, which add trainable lightweight layers to the existing pretrained weights. Within this context, we propose AdaKron, an Adapter-based fine-tuning with the Kronecker product. In particular, we leverage the Kronecker product to combine the output of two small networks, resulting in a final vector whose dimension is the product of the dimensions of the individual outputs, allowing us to train only 0.55{\%} of the model{'}s original parameters. We evaluate AdaKron performing a series of experiments on the General Language Understanding Evaluation (GLUE) benchmark, achieving results in the same ballpark as recent state-of-the-art PEFT methods, despite training fewer parameters.",
}
| The fine-tuning paradigm has been widely adopted to train neural models tailored for specific tasks. However, the recent upsurge of Large Language Models (LLMs), characterized by billions of parameters, has introduced profound computational challenges to the fine-tuning process. This has fueled intensive research on Parameter-Efficient Fine-Tuning (PEFT) techniques, usually involving the training of a selective subset of the original model parameters. One of the most used approaches is Adapters, which add trainable lightweight layers to the existing pretrained weights. Within this context, we propose AdaKron, an Adapter-based fine-tuning with the Kronecker product. In particular, we leverage the Kronecker product to combine the output of two small networks, resulting in a final vector whose dimension is the product of the dimensions of the individual outputs, allowing us to train only 0.55{\%} of the model{'}s original parameters. We evaluate AdaKron performing a series of experiments on the General Language Understanding Evaluation (GLUE) benchmark, achieving results in the same ballpark as recent state-of-the-art PEFT methods, despite training fewer parameters. | [
"Braga, Marco",
"Raganato, Aless",
"ro",
"Pasi, Gabriella"
] | AdaKron: An Adapter-based Parameter Efficient Model Tuning with Kronecker Product | lrec-main.32 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.33.bib | https://aclanthology.org/2024.lrec-main.33/ | @inproceedings{xu-etal-2024-adaptive,
title = "Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation",
author = "Xu, Bo and
Wu, Yifei and
Wei, Shouang and
Du, Ming and
Wang, Hongya",
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.33",
pages = "358--371",
abstract = "Sentence representation learning is a fundamental task in NLP. Existing methods use contrastive learning (CL) to learn effective sentence representations, which benefit from high-quality contrastive data but require extensive human annotation. Large language models (LLMs) like ChatGPT and GPT4 can automatically generate such data. However, this alternative strategy also encounters challenges: 1) obtaining high-quality generated data from small-parameter LLMs is difficult, and 2) inefficient utilization of the generated data. To address these challenges, we propose a novel adaptive reinforcement tuning (ART) framework. Specifically, to address the first challenge, we introduce a reinforcement learning approach for fine-tuning small-parameter LLMs, enabling the generation of high-quality hard contrastive data without human feedback. To address the second challenge, we propose an adaptive iterative framework to guide the small-parameter LLMs to generate progressively harder samples through multiple iterations, thereby maximizing the utility of generated data. Experiments conducted on seven semantic text similarity tasks demonstrate that the sentence representation models trained using the synthetic data generated by our proposed method achieve state-of-the-art performance. Our code is available at https://github.com/WuNein/AdaptCL.",
}
| Sentence representation learning is a fundamental task in NLP. Existing methods use contrastive learning (CL) to learn effective sentence representations, which benefit from high-quality contrastive data but require extensive human annotation. Large language models (LLMs) like ChatGPT and GPT4 can automatically generate such data. However, this alternative strategy also encounters challenges: 1) obtaining high-quality generated data from small-parameter LLMs is difficult, and 2) inefficient utilization of the generated data. To address these challenges, we propose a novel adaptive reinforcement tuning (ART) framework. Specifically, to address the first challenge, we introduce a reinforcement learning approach for fine-tuning small-parameter LLMs, enabling the generation of high-quality hard contrastive data without human feedback. To address the second challenge, we propose an adaptive iterative framework to guide the small-parameter LLMs to generate progressively harder samples through multiple iterations, thereby maximizing the utility of generated data. Experiments conducted on seven semantic text similarity tasks demonstrate that the sentence representation models trained using the synthetic data generated by our proposed method achieve state-of-the-art performance. Our code is available at https://github.com/WuNein/AdaptCL. | [
"Xu, Bo",
"Wu, Yifei",
"Wei, Shouang",
"Du, Ming",
"Wang, Hongya"
] | Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation | lrec-main.33 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.34.bib | https://aclanthology.org/2024.lrec-main.34/ | @inproceedings{sun-etal-2024-adaptive,
title = "Adaptive Simultaneous Sign Language Translation with Confident Translation Length Estimation",
author = "Sun, Tong and
Fu, Biao and
Hu, Cong and
Zhang, Liang and
Zhang, Ruiquan and
Shi, Xiaodong and
Su, Jinsong and
Chen, Yidong",
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.34",
pages = "372--384",
abstract = "Traditional non-simultaneous Sign Language Translation (SLT) methods, while effective for pre-recorded videos, face challenges in real-time scenarios due to inherent inference delays. The emerging field of simultaneous SLT aims to address this issue by progressively translating incrementally received sign video. However, the sole existing work in simultaneous SLT adopts a fixed gloss-based policy, which suffer from limitations in boundary prediction and contextual comprehension. In this paper, we delve deeper into this area and propose an adaptive policy for simultaneous SLT. Our approach introduces the concept of {``}confident translation length{''}, denoting maximum accurate translation achievable from current input. An estimator measures this length for streaming sign video, enabling the model to make informed decisions on whether to wait for more input or proceed with translation. To train the estimator, we construct a training data of confident translation length based on the longest common prefix between translations of partial and complete inputs. Furthermore, we incorporate adaptive training, utilizing pseudo prefix pairs, to refine the offline translation model for optimal performance in simultaneous scenarios. Experimental results on PHOENIX2014T and CSL-Daily demonstrate the superiority of our adaptive policy over existing methods, particularly excelling in situations requiring extremely low latency.",
}
| Traditional non-simultaneous Sign Language Translation (SLT) methods, while effective for pre-recorded videos, face challenges in real-time scenarios due to inherent inference delays. The emerging field of simultaneous SLT aims to address this issue by progressively translating incrementally received sign video. However, the sole existing work in simultaneous SLT adopts a fixed gloss-based policy, which suffer from limitations in boundary prediction and contextual comprehension. In this paper, we delve deeper into this area and propose an adaptive policy for simultaneous SLT. Our approach introduces the concept of {``}confident translation length{''}, denoting maximum accurate translation achievable from current input. An estimator measures this length for streaming sign video, enabling the model to make informed decisions on whether to wait for more input or proceed with translation. To train the estimator, we construct a training data of confident translation length based on the longest common prefix between translations of partial and complete inputs. Furthermore, we incorporate adaptive training, utilizing pseudo prefix pairs, to refine the offline translation model for optimal performance in simultaneous scenarios. Experimental results on PHOENIX2014T and CSL-Daily demonstrate the superiority of our adaptive policy over existing methods, particularly excelling in situations requiring extremely low latency. | [
"Sun, Tong",
"Fu, Biao",
"Hu, Cong",
"Zhang, Liang",
"Zhang, Ruiquan",
"Shi, Xiaodong",
"Su, Jinsong",
"Chen, Yidong"
] | Adaptive Simultaneous Sign Language Translation with Confident Translation Length Estimation | lrec-main.34 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.35.bib | https://aclanthology.org/2024.lrec-main.35/ | @inproceedings{blouin-etal-2024-dataset,
title = "A Dataset for Named Entity Recognition and Entity Linking in {C}hinese Historical Newspapers",
author = "Blouin, Baptiste and
Armand, C{\'e}cile and
Henriot, Christian",
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.35",
pages = "385--394",
abstract = "In this study, we present a novel historical Chinese dataset for named entity recognition, entity linking, coreference and entity relations. We use data from Chinese newspapers from 1872 to 1949 and multilingual bibliographic resources from the same period. The period and the language are the main strength of the present work, offering a resource which covers different styles and language uses, as well as the largest historical Chinese NER dataset with manual annotations from this transitional period. After detailing the selection and annotation process, we present the very first results that can be obtained from this dataset. Texts and annotations are freely downloadable from the GitHub repository.",
}
| In this study, we present a novel historical Chinese dataset for named entity recognition, entity linking, coreference and entity relations. We use data from Chinese newspapers from 1872 to 1949 and multilingual bibliographic resources from the same period. The period and the language are the main strength of the present work, offering a resource which covers different styles and language uses, as well as the largest historical Chinese NER dataset with manual annotations from this transitional period. After detailing the selection and annotation process, we present the very first results that can be obtained from this dataset. Texts and annotations are freely downloadable from the GitHub repository. | [
"Blouin, Baptiste",
"Arm",
", C{\\'e}cile",
"Henriot, Christian"
] | A Dataset for Named Entity Recognition and Entity Linking in Chinese Historical Newspapers | lrec-main.35 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.36.bib | https://aclanthology.org/2024.lrec-main.36/ | @inproceedings{raithel-etal-2024-dataset,
title = "A Dataset for Pharmacovigilance in {G}erman, {F}rench, and {J}apanese: Annotating Adverse Drug Reactions across Languages",
author = {Raithel, Lisa and
Yeh, Hui-Syuan and
Yada, Shuntaro and
Grouin, Cyril and
Lavergne, Thomas and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Paroubek, Patrick and
Thomas, Philippe and
Nishiyama, Tomohiro and
M{\"o}ller, Sebastian and
Aramaki, Eiji and
Matsumoto, Yuji and
Roller, Roland and
Zweigenbaum, Pierre},
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.36",
pages = "395--414",
abstract = "User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages.",
}
| User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts concerning ADRs gathered from diverse sources, including patient fora, social media, and clinical reports in German, French, and Japanese. Our corpus contains annotations covering 12 entity types, four attribute types, and 13 relation types. It contributes to the development of real-world multilingual language models for healthcare. We provide statistics to highlight certain challenges associated with the corpus and conduct preliminary experiments resulting in strong baselines for extracting entities and relations between these entities, both within and across languages. | [
"Raithel, Lisa",
"Yeh, Hui-Syuan",
"Yada, Shuntaro",
"Grouin, Cyril",
"Lavergne, Thomas",
"N{\\'e}v{\\'e}ol, Aur{\\'e}lie",
"Paroubek, Patrick",
"Thomas, Philippe",
"Nishiyama, Tomohiro",
"M{\\\"o}ller, Sebastian",
"Aramaki, Eiji",
"Matsumoto, Yuji",
"Roller, Rol",
"",
"Zweigenbaum, Pierre"
] | A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages | lrec-main.36 | Poster | 2403.18336 | [
"https://github.com/dotkat-dotcome/keepha-adr"
] | https://huggingface.co/papers/2403.18336 | 0 | 0 | 0 | 14 | 1 | [] | [] | [] |
https://aclanthology.org/2024.lrec-main.37.bib | https://aclanthology.org/2024.lrec-main.37/ | @inproceedings{kumar-etal-2024-adding,
title = "Adding {SPICE} to Life: Speaker Profiling in Multiparty Conversations",
author = "Kumar, Shivani and
Gupta, Rishabh and
Akhtar, Md. Shad and
Chakraborty, Tanmoy",
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.37",
pages = "415--425",
abstract = "In the realm of conversational dynamics, individual idiosyncrasies challenge the suitability of a one-size-fits-all approach for dialogue agent responses. Prior studies often assumed the speaker{'}s persona{'}s immediate availability, a premise not universally applicable. To address this gap, we explore the Speaker Profiling in Conversations (SPC) task, aiming to synthesize persona attributes for each dialogue participant. SPC comprises three core subtasks: persona discovery, persona-type identification, and persona-value extraction. The first subtask identifies persona-related utterances, the second classifies specific attributes, and the third extracts precise values for the persona. To confront this multifaceted challenge, we{'}ve diligently compiled SPICE, an annotated dataset, underpinning our thorough evaluation of diverse baseline models. Additionally, we benchmark these findings against our innovative neural model, SPOT, presenting an exhaustive analysis encompassing a nuanced assessment of quantitative and qualitative merits and limitations.",
}
| In the realm of conversational dynamics, individual idiosyncrasies challenge the suitability of a one-size-fits-all approach for dialogue agent responses. Prior studies often assumed the speaker{'}s persona{'}s immediate availability, a premise not universally applicable. To address this gap, we explore the Speaker Profiling in Conversations (SPC) task, aiming to synthesize persona attributes for each dialogue participant. SPC comprises three core subtasks: persona discovery, persona-type identification, and persona-value extraction. The first subtask identifies persona-related utterances, the second classifies specific attributes, and the third extracts precise values for the persona. To confront this multifaceted challenge, we{'}ve diligently compiled SPICE, an annotated dataset, underpinning our thorough evaluation of diverse baseline models. Additionally, we benchmark these findings against our innovative neural model, SPOT, presenting an exhaustive analysis encompassing a nuanced assessment of quantitative and qualitative merits and limitations. | [
"Kumar, Shivani",
"Gupta, Rishabh",
"Akhtar, Md. Shad",
"Chakraborty, Tanmoy"
] | Adding SPICE to Life: Speaker Profiling in Multiparty Conversations | lrec-main.37 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.38.bib | https://aclanthology.org/2024.lrec-main.38/ | @inproceedings{hauptmann-etal-2024-adea,
title = "{ADEA}: An Argumentative Dialogue Dataset on Ethical Issues Concerning Future {A}.{I}. Applications",
author = "Hauptmann, Christian and
Krenzer, Adrian and
Krause, Antonia and
Puppe, 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.38",
pages = "426--437",
abstract = "Introducing ADEA: a German dataset that captures online dialogues and focuses on ethical issues related to future AI applications. This dataset, which includes over 2800 labeled user utterances on four different topics, is specifically designed for the training of chatbots that can navigate the complexities of real-world ethical AI conversations. The creation of these dialogues is the result of two carefully conducted studies in which university students interacted with an argumentative dialogue system. A fundamental part of our methodology is the use of German argument graphs. These graphs not only form the knowledge base of the dialogue system but also serve as an effective annotation scheme for the dialogues. Apart from the introduction of the dataset and the argument graphs, we provide a preliminary benchmark using GPT-4 via the OpenAI API. This provides researchers with a concrete reference point while demonstrating the potential of our dataset. We make our dataset and argument graphs available at https://github.com/HaupChris/ADEA-Dialogue-Dataset.",
}
| Introducing ADEA: a German dataset that captures online dialogues and focuses on ethical issues related to future AI applications. This dataset, which includes over 2800 labeled user utterances on four different topics, is specifically designed for the training of chatbots that can navigate the complexities of real-world ethical AI conversations. The creation of these dialogues is the result of two carefully conducted studies in which university students interacted with an argumentative dialogue system. A fundamental part of our methodology is the use of German argument graphs. These graphs not only form the knowledge base of the dialogue system but also serve as an effective annotation scheme for the dialogues. Apart from the introduction of the dataset and the argument graphs, we provide a preliminary benchmark using GPT-4 via the OpenAI API. This provides researchers with a concrete reference point while demonstrating the potential of our dataset. We make our dataset and argument graphs available at https://github.com/HaupChris/ADEA-Dialogue-Dataset. | [
"Hauptmann, Christian",
"Krenzer, Adrian",
"Krause, Antonia",
"Puppe, Frank"
] | ADEA: An Argumentative Dialogue Dataset on Ethical Issues Concerning Future A.I. Applications | lrec-main.38 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.39.bib | https://aclanthology.org/2024.lrec-main.39/ | @inproceedings{turki-etal-2024-decade,
title = "A Decade of Scholarly Research on Open Knowledge Graphs",
author = "Turki, Houcemeddine and
Owodunni, Abraham Toluwase and
Hadj Taieb, Mohamed Ali and
Bile, Ren{\'e} Fabrice and
Ben Aouicha, 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.39",
pages = "438--448",
abstract = "The proliferation of open knowledge graphs has led to a surge in scholarly research on the topic over the past decade. This paper presents a bibliometric analysis of the scholarly literature on open knowledge graphs published between 2013 and 2023. The study aims to identify the trends, patterns, and impact of research in this field, as well as the key topics and research questions that have emerged. The work uses bibliometric techniques to analyze a sample of 4445 scholarly articles retrieved from Scopus. The findings reveal an ever-increasing number of publications on open knowledge graphs published every year, particularly in developed countries (+50 per year). These outputs are published in highly-referred scholarly journals and conferences. The study identifies three main research themes: (1) knowledge graph construction and enrichment, (2) evaluation and reuse, and (3) fusion of knowledge graphs into NLP systems. Within these themes, the study identifies specific tasks that have received considerable attention, including entity linking, knowledge graph embedding, and graph neural networks.",
}
| The proliferation of open knowledge graphs has led to a surge in scholarly research on the topic over the past decade. This paper presents a bibliometric analysis of the scholarly literature on open knowledge graphs published between 2013 and 2023. The study aims to identify the trends, patterns, and impact of research in this field, as well as the key topics and research questions that have emerged. The work uses bibliometric techniques to analyze a sample of 4445 scholarly articles retrieved from Scopus. The findings reveal an ever-increasing number of publications on open knowledge graphs published every year, particularly in developed countries (+50 per year). These outputs are published in highly-referred scholarly journals and conferences. The study identifies three main research themes: (1) knowledge graph construction and enrichment, (2) evaluation and reuse, and (3) fusion of knowledge graphs into NLP systems. Within these themes, the study identifies specific tasks that have received considerable attention, including entity linking, knowledge graph embedding, and graph neural networks. | [
"Turki, Houcemeddine",
"Owodunni, Abraham Toluwase",
"Hadj Taieb, Mohamed Ali",
"Bile, Ren{\\'e} Fabrice",
"Ben Aouicha, Mohamed"
] | A Decade of Scholarly Research on Open Knowledge Graphs | lrec-main.39 | Poster | 2306.13186 | [
"https://github.com/data-engineering-and-semantics/openkgbiblio"
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.40.bib | https://aclanthology.org/2024.lrec-main.40/ | @inproceedings{thayaparan-etal-2024-differentiable,
title = "A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference",
author = "Thayaparan, Mokanarangan and
Valentino, Marco and
Freitas, Andr{\'e}",
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.40",
pages = "449--458",
abstract = "Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI). However, traditional ILP frameworks are non-differentiable, posing critical challenges for the integration of continuous language representations based on deep learning. In this paper, we introduce a novel approach, named Diff-Comb Explainer, a neuro-symbolic architecture for explanation-based NLI based on Differentiable BlackBox Combinatorial Solvers (DBCS). Differently from existing neuro-symbolic solvers, Diff-Comb Explainer does not necessitate a continuous relaxation of the semantic constraints, enabling a direct, more precise, and efficient incorporation of neural representations into the ILP formulation. Our experiments demonstrate that Diff-Comb Explainer achieves superior performance when compared to conventional ILP solvers, neuro-symbolic black-box solvers, and Transformer-based encoders. Moreover, a deeper analysis reveals that Diff-Comb Explainer can significantly improve the precision, consistency, and faithfulness of the constructed explanations, opening new opportunities for research on neuro-symbolic architectures for explainable and transparent NLI in complex domains.",
}
| Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI). However, traditional ILP frameworks are non-differentiable, posing critical challenges for the integration of continuous language representations based on deep learning. In this paper, we introduce a novel approach, named Diff-Comb Explainer, a neuro-symbolic architecture for explanation-based NLI based on Differentiable BlackBox Combinatorial Solvers (DBCS). Differently from existing neuro-symbolic solvers, Diff-Comb Explainer does not necessitate a continuous relaxation of the semantic constraints, enabling a direct, more precise, and efficient incorporation of neural representations into the ILP formulation. Our experiments demonstrate that Diff-Comb Explainer achieves superior performance when compared to conventional ILP solvers, neuro-symbolic black-box solvers, and Transformer-based encoders. Moreover, a deeper analysis reveals that Diff-Comb Explainer can significantly improve the precision, consistency, and faithfulness of the constructed explanations, opening new opportunities for research on neuro-symbolic architectures for explainable and transparent NLI in complex domains. | [
"Thayaparan, Mokanarangan",
"Valentino, Marco",
"Freitas, Andr{\\'e}"
] | A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference | lrec-main.40 | Poster | 2404.02625 | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.41.bib | https://aclanthology.org/2024.lrec-main.41/ | @inproceedings{nagai-etal-2024-document,
title = "A Document-Level Text Simplification Dataset for {J}apanese",
author = "Nagai, Yoshinari and
Oka, Teruaki and
Komachi, Mamoru",
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.41",
pages = "459--476",
abstract = "Document-level text simplification, a task that combines single-document summarization and intra-sentence simplification, has garnered significant attention. However, studies have primarily focused on languages such as English and German, leaving Japanese and similar languages underexplored because of a scarcity of linguistic resources. In this study, we devised JADOS, the first Japanese document-level text simplification dataset based on newspaper articles and Wikipedia. Our dataset focuses on simplification, to enhance readability by reducing the number of sentences and tokens in a document. We conducted investigations using our dataset. Firstly, we analyzed the characteristics of Japanese simplification by comparing it across different domains and with English counterparts. Moreover, we experimentally evaluated the performances of text summarization methods, transformer-based text simplification models, and large language models. In terms of D-SARI scores, the transformer-based models performed best across all domains. Finally, we manually evaluated several model outputs and target articles, demonstrating the need for document-level text simplification models in Japanese.",
}
| Document-level text simplification, a task that combines single-document summarization and intra-sentence simplification, has garnered significant attention. However, studies have primarily focused on languages such as English and German, leaving Japanese and similar languages underexplored because of a scarcity of linguistic resources. In this study, we devised JADOS, the first Japanese document-level text simplification dataset based on newspaper articles and Wikipedia. Our dataset focuses on simplification, to enhance readability by reducing the number of sentences and tokens in a document. We conducted investigations using our dataset. Firstly, we analyzed the characteristics of Japanese simplification by comparing it across different domains and with English counterparts. Moreover, we experimentally evaluated the performances of text summarization methods, transformer-based text simplification models, and large language models. In terms of D-SARI scores, the transformer-based models performed best across all domains. Finally, we manually evaluated several model outputs and target articles, demonstrating the need for document-level text simplification models in Japanese. | [
"Nagai, Yoshinari",
"Oka, Teruaki",
"Komachi, Mamoru"
] | A Document-Level Text Simplification Dataset for Japanese | lrec-main.41 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.42.bib | https://aclanthology.org/2024.lrec-main.42/ | @inproceedings{han-etal-2024-dual,
title = "A Dual-View Approach to Classifying Radiology Reports by Co-Training",
author = "Han, Yutong and
Yuan, Yan and
Mou, Lili",
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.42",
pages = "477--483",
abstract = "Radiology report analysis provides valuable information that can aid with public health initiatives, and has been attracting increasing attention from the research community. In this work, we present a novel insight that the structure of a radiology report (namely, the Findings and Impression sections) offers different views of a radiology scan. Based on this intuition, we further propose a co-training approach, where two machine learning models are built upon the Findings and Impression sections, respectively, and use each other{'}s information to boost performance with massive unlabeled data in a semi-supervised manner. We conducted experiments in a public health surveillance study, and results show that our co-training approach is able to improve performance using the dual views and surpass competing supervised and semi-supervised methods.",
}
| Radiology report analysis provides valuable information that can aid with public health initiatives, and has been attracting increasing attention from the research community. In this work, we present a novel insight that the structure of a radiology report (namely, the Findings and Impression sections) offers different views of a radiology scan. Based on this intuition, we further propose a co-training approach, where two machine learning models are built upon the Findings and Impression sections, respectively, and use each other{'}s information to boost performance with massive unlabeled data in a semi-supervised manner. We conducted experiments in a public health surveillance study, and results show that our co-training approach is able to improve performance using the dual views and surpass competing supervised and semi-supervised methods. | [
"Han, Yutong",
"Yuan, Yan",
"Mou, Lili"
] | A Dual-View Approach to Classifying Radiology Reports by Co-Training | lrec-main.42 | Poster | 2406.05995 | [
"https://github.com/manga-uofa/radiology-cotrain"
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.43.bib | https://aclanthology.org/2024.lrec-main.43/ | @inproceedings{lee-etal-2024-advancing,
title = "Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms",
author = "Lee, Wonkee and
Heo, Seong-Hwan and
Lee, Jong-Hyeok",
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.43",
pages = "484--494",
abstract = "Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing (APE) models due to the lack of training data. With this aim, we focus on data-synthesis methods to create high-quality synthetic data. Given that APE takes as input a machine-translation result that might include errors, we present a data-synthesis method by which the resulting synthetic data mimic the translation errors found in actual data. We introduce a noising-based data-synthesis method by adapting the masked language model approach, generating a noisy text from a clean text by infilling masked tokens with erroneous tokens. Moreover, we propose selective corpus interleaving that combines two separate synthetic datasets by taking only the advantageous samples to enhance the quality of the synthetic data further. Experimental results show that using the synthetic data created by our approach results in significantly better APE performance than other synthetic data created by existing methods.",
}
| Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing (APE) models due to the lack of training data. With this aim, we focus on data-synthesis methods to create high-quality synthetic data. Given that APE takes as input a machine-translation result that might include errors, we present a data-synthesis method by which the resulting synthetic data mimic the translation errors found in actual data. We introduce a noising-based data-synthesis method by adapting the masked language model approach, generating a noisy text from a clean text by infilling masked tokens with erroneous tokens. Moreover, we propose selective corpus interleaving that combines two separate synthetic datasets by taking only the advantageous samples to enhance the quality of the synthetic data further. Experimental results show that using the synthetic data created by our approach results in significantly better APE performance than other synthetic data created by existing methods. | [
"Lee, Wonkee",
"Heo, Seong-Hwan",
"Lee, Jong-Hyeok"
] | Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms | lrec-main.43 | Poster | 2204.03896 | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.44.bib | https://aclanthology.org/2024.lrec-main.44/ | @inproceedings{jiang-etal-2024-advancing,
title = "Advancing Topic Segmentation and Outline Generation in {C}hinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark",
author = "Jiang, Feng and
Liu, Weihao and
Chu, Xiaomin and
Li, Peifeng and
Zhu, Qiaoming and
Li, Haizhou",
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.44",
pages = "495--506",
abstract = "Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings, unveiling the discourse topic structure of a document. Compared with sentence-level topic structure, the paragraph-level topic structure can quickly grasp and understand the overall context of the document from a higher level, benefitting many downstream tasks such as summarization, discourse parsing, and information retrieval. However, the lack of large-scale, high-quality Chinese paragraph-level topic structure corpora restrained relative research and applications. To fill this gap, we build the Chinese paragraph-level topic representation, corpus, and benchmark in this paper. Firstly, we propose a hierarchical paragraph-level topic structure representation with three layers to guide the corpus construction. Then, we employ a two-stage man-machine collaborative annotation method to construct the largest Chinese Paragraph-level Topic Structure corpus (CPTS), achieving high quality. We also build several strong baselines, including ChatGPT, to validate the computability of CPTS on two fundamental tasks (topic segmentation and outline generation) and preliminarily verified its usefulness for the downstream task (discourse parsing).",
}
| Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings, unveiling the discourse topic structure of a document. Compared with sentence-level topic structure, the paragraph-level topic structure can quickly grasp and understand the overall context of the document from a higher level, benefitting many downstream tasks such as summarization, discourse parsing, and information retrieval. However, the lack of large-scale, high-quality Chinese paragraph-level topic structure corpora restrained relative research and applications. To fill this gap, we build the Chinese paragraph-level topic representation, corpus, and benchmark in this paper. Firstly, we propose a hierarchical paragraph-level topic structure representation with three layers to guide the corpus construction. Then, we employ a two-stage man-machine collaborative annotation method to construct the largest Chinese Paragraph-level Topic Structure corpus (CPTS), achieving high quality. We also build several strong baselines, including ChatGPT, to validate the computability of CPTS on two fundamental tasks (topic segmentation and outline generation) and preliminarily verified its usefulness for the downstream task (discourse parsing). | [
"Jiang, Feng",
"Liu, Weihao",
"Chu, Xiaomin",
"Li, Peifeng",
"Zhu, Qiaoming",
"Li, Haizhou"
] | Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark | lrec-main.44 | Poster | 2305.14790 | [
"https://github.com/fjiangai/cpts"
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.45.bib | https://aclanthology.org/2024.lrec-main.45/ | @inproceedings{zmitrovich-etal-2024-family,
title = "A Family of Pretrained Transformer Language Models for {R}ussian",
author = "Zmitrovich, Dmitry and
Abramov, Aleksandr and
Kalmykov, Andrey and
Kadulin, Vitaly and
Tikhonova, Maria and
Taktasheva, Ekaterina and
Astafurov, Danil and
Baushenko, Mark and
Snegirev, Artem and
Shavrina, Tatiana and
Markov, Sergei S. and
Mikhailov, Vladislav and
Fenogenova, Alena",
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.45",
pages = "507--524",
abstract = "Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages. However, developing such models specifically for the Russian language has received little attention. This paper introduces a collection of 13 Russian Transformer LMs, which spans encoder (ruBERT, ruRoBERTa, ruELECTRA), decoder (ruGPT-3), and encoder-decoder (ruT5, FRED-T5) architectures. We provide a report on the model architecture design and pretraining, and the results of evaluating their generalization abilities on Russian language understanding and generation datasets and benchmarks. By pretraining and releasing these specialized Transformer LMs, we aim to broaden the scope of the NLP research directions and enable the development of industrial solutions for the Russian language.",
}
| Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages. However, developing such models specifically for the Russian language has received little attention. This paper introduces a collection of 13 Russian Transformer LMs, which spans encoder (ruBERT, ruRoBERTa, ruELECTRA), decoder (ruGPT-3), and encoder-decoder (ruT5, FRED-T5) architectures. We provide a report on the model architecture design and pretraining, and the results of evaluating their generalization abilities on Russian language understanding and generation datasets and benchmarks. By pretraining and releasing these specialized Transformer LMs, we aim to broaden the scope of the NLP research directions and enable the development of industrial solutions for the Russian language. | [
"Zmitrovich, Dmitry",
"Abramov, Aleks",
"r",
"Kalmykov, Andrey",
"Kadulin, Vitaly",
"Tikhonova, Maria",
"Taktasheva, Ekaterina",
"Astafurov, Danil",
"Baushenko, Mark",
"Snegirev, Artem",
"Shavrina, Tatiana",
"Markov, Sergei S.",
"Mikhailov, Vladislav",
"Fenogenova, Alena"
] | A Family of Pretrained Transformer Language Models for Russian | lrec-main.45 | Poster | 2309.10931 | [
""
] | https://huggingface.co/papers/2309.10931 | 2 | 3 | 0 | 12 | 1 | [
"ai-forever/rugpt3large_based_on_gpt2",
"ai-forever/FRED-T5-1.7B",
"ai-forever/ruRoberta-large",
"ai-forever/ruT5-large",
"ai-forever/rugpt3small_based_on_gpt2",
"ai-forever/ruBert-base",
"ai-forever/FRED-T5-large",
"ai-forever/rugpt3medium_based_on_gpt2",
"ai-forever/ruT5-base",
"ai-forever/ruBert-large",
"ai-forever/ruElectra-small",
"ai-forever/ruElectra-medium",
"ai-forever/ruElectra-large",
"DFofanov78/rugpt3small_based_on_gpt2",
"DFofanov78/rugpt3large_based_on_gpt2",
"DFofanov78/rugpt3medium_based_on_gpt2",
"Gnider/model_old_working"
] | [] | [
"open-llm-leaderboard/open_llm_leaderboard",
"Intel/low_bit_open_llm_leaderboard",
"BAAI/open_cn_llm_leaderboard",
"gsaivinay/open_llm_leaderboard",
"GTBench/GTBench",
"big-kek/NeuroKorzh",
"felixz/open_llm_leaderboard",
"OPTML-Group/UnlearnCanvas-Benchmark",
"Vikhrmodels/small-shlepa-lb",
"AlexWortega/ruImageCaptionong",
"end000/sberbank-ai-FRED-T5-1.7B",
"b1sheng/kg_llm_leaderboard_test",
"fkonovalenko/llm4career",
"AlekseyKorshuk/rugpt3",
"kllmagn/sberbank-ai-rugpt3large_based_on_gpt2",
"kamakepar/sberbank-ai-rugpt3large",
"kamakepar/sberbank-ai-rugpt3large_based_on_gpt2",
"neubla/neubla-llm-evaluation-board",
"rodrigomasini/data_only_open_llm_leaderboard",
"Docfile/open_llm_leaderboard",
"alGOriTM207/Ru_DialoModel",
"MesonWarrior/vk",
"Anonumous/RuImageCaptioning",
"AntNikYab/NaturalLanguageProcessing",
"jeydipak/nlp_project",
"Andrew3875/ai-forever-FRED-T5-large",
"orzhan/ruatd",
"Lowgreatahm/ai-forever-ruRoberta-large",
"yturkunov/finRecommender",
"4eJIoBek/ruGPT3-Large",
"Crits/ai-forever-rugpt3large_based_on_gpt2",
"Heleg/pt2",
"dokster/vqa-analysis",
"eteron/Dialogue_assistant",
"smothiki/open_llm_leaderboard",
"pngwn/open_llm_leaderboard",
"ultrin/nameform-ru",
"pngwn/open_llm_leaderboard_two",
"choco9966/LeaderboardTest",
"0x1668/open_llm_leaderboard",
"pngwn/open_llm_leaderboard-check",
"R-uslan/GPTRUS",
"asir0z/open_llm_leaderboard",
"kbmlcoding/open_llm_leaderboard_free",
"choco9966/open-ko-llm-leaderboard",
"uhygfd/GPT2",
"aichampions/open_llm_leaderboard",
"Adeco/open_llm_leaderboard",
"anirudh937/open_llm_leaderboard",
"smothiki/open_llm_leaderboard2",
"4eJIoBek/ruGPT3-Medium",
"Jorj2064/gpt_chat_1234",
"Jorj2064/kge_bot",
"loveto/shad_transformers",
"A1ex1/text-generation",
"4eJIoBek/ruGPT3-Small",
"Serg4451D/RuGPT",
"vasevooo/NLP_project",
"SaviAnna/history_mistery",
"Ilvir/ilva",
"vvv-knyazeva/NLP_project",
"SaviAnna/History",
"ruslanruslanruslan/nlp_project",
"Vladislawoo/nlp-gpt-team",
"alizhgir/ds-prj-10-w",
"RMakushkin/test_space",
"HaggiVaggi/nlp_project",
"derat0r/derat0r_test_space",
"DuckyPolice/ChatStormAI",
"Norgan97/forjobtwo",
"Ivan1579/ai-forever-rugpt3small_based_on_gpt2",
"IvT-DS/nlp_proj",
"Maslov-Artem/nlp_proj",
"Shchushch/CV",
"Unavailable1/Adaptive_reading_assessment",
"Solar-Iz/ds-prj-10-w",
"ds-meteors/nlp-lstm-team",
"NLPLSTMteam/NLP_LSTM_team",
"ElbrusGPT/elbrus_text_project",
"Andriano2323/NLP_LSTM_team",
"Kdnv/nlp_project",
"Seppukku/nlp_project_gpt_team"
] |
https://aclanthology.org/2024.lrec-main.46.bib | https://aclanthology.org/2024.lrec-main.46/ | @inproceedings{tao-etal-2024-fast,
title = "A Fast and High-quality Text-to-Speech Method with Compressed Auxiliary Corpus and Limited Target Speaker Corpus",
author = "Tao, Ye and
Lu, Chaofeng and
Liu, Meng and
Xu, Kai and
Liu, Tianyu and
Tian, Yunlong and
Du, Yongjie",
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.46",
pages = "525--535",
abstract = "With an auxiliary corpus (non-target speaker corpus) for model pre-training, Text-to-Speech (TTS) methods can generate high-quality speech with a limited target speaker corpus. However, this approach comes with expensive training costs. To overcome the challenge, a high-quality TTS method is proposed, significantly reducing training costs while maintaining the naturalness of synthesized speech. In this paper, we propose an auxiliary corpus compression algorithm that reduces the training cost while the naturalness of the synthesized speech is not significantly degraded. We then use the compressed corpus to pre-train the proposed TTS model CMDTTS, which fuses phoneme and word multi-level prosody modeling components and denoises the generated mel-spectrograms using denoising diffusion probabilistic models (DDPMs). In addition, a fine-tuning step that the conditional generative adversarial network (cGAN) is introduced to embed the target speaker feature and improve speech quality using the target speaker corpus. Experiments are conducted on Chinese and English single speaker{'}s corpora, and the results show that the method effectively balances the model training speed and the synthesized speech quality and outperforms the current models.",
}
| With an auxiliary corpus (non-target speaker corpus) for model pre-training, Text-to-Speech (TTS) methods can generate high-quality speech with a limited target speaker corpus. However, this approach comes with expensive training costs. To overcome the challenge, a high-quality TTS method is proposed, significantly reducing training costs while maintaining the naturalness of synthesized speech. In this paper, we propose an auxiliary corpus compression algorithm that reduces the training cost while the naturalness of the synthesized speech is not significantly degraded. We then use the compressed corpus to pre-train the proposed TTS model CMDTTS, which fuses phoneme and word multi-level prosody modeling components and denoises the generated mel-spectrograms using denoising diffusion probabilistic models (DDPMs). In addition, a fine-tuning step that the conditional generative adversarial network (cGAN) is introduced to embed the target speaker feature and improve speech quality using the target speaker corpus. Experiments are conducted on Chinese and English single speaker{'}s corpora, and the results show that the method effectively balances the model training speed and the synthesized speech quality and outperforms the current models. | [
"Tao, Ye",
"Lu, Chaofeng",
"Liu, Meng",
"Xu, Kai",
"Liu, Tianyu",
"Tian, Yunlong",
"Du, Yongjie"
] | A Fast and High-quality Text-to-Speech Method with Compressed Auxiliary Corpus and Limited Target Speaker Corpus | lrec-main.46 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.47.bib | https://aclanthology.org/2024.lrec-main.47/ | @inproceedings{yang-etal-2024-frustratingly,
title = "A Frustratingly Simple Decoding Method for Neural Text Generation",
author = "Yang, Haoran and
Cai, Deng and
Li, Huayang and
Bi, Wei and
Lam, Wai and
Shi, Shuming",
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.47",
pages = "536--557",
abstract = "We introduce a frustratingly simple, highly efficient, and surprisingly effective decoding method, termed Frustratingly Simple Decoding (FSD), for neural text generation. The idea behind FSD is straightforward: We construct an anti-language model (anti-LM) based on previously generated text, which is employed to penalize the future generation of repetitive content. The anti-LM can be implemented as simple as an n-gram language model or a vectorized variant. In this way, FSD incurs no additional model parameters and negligible computational overhead (FSD can be as fast as greedy search). Despite its simplicity, FSD is surprisingly effective and generalizes across different datasets, models, and languages. Extensive experiments show that FSD outperforms established strong baselines in terms of generation quality, decoding speed, and universality.",
}
| We introduce a frustratingly simple, highly efficient, and surprisingly effective decoding method, termed Frustratingly Simple Decoding (FSD), for neural text generation. The idea behind FSD is straightforward: We construct an anti-language model (anti-LM) based on previously generated text, which is employed to penalize the future generation of repetitive content. The anti-LM can be implemented as simple as an n-gram language model or a vectorized variant. In this way, FSD incurs no additional model parameters and negligible computational overhead (FSD can be as fast as greedy search). Despite its simplicity, FSD is surprisingly effective and generalizes across different datasets, models, and languages. Extensive experiments show that FSD outperforms established strong baselines in terms of generation quality, decoding speed, and universality. | [
"Yang, Haoran",
"Cai, Deng",
"Li, Huayang",
"Bi, Wei",
"Lam, Wai",
"Shi, Shuming"
] | A Frustratingly Simple Decoding Method for Neural Text Generation | lrec-main.47 | Poster | 2305.12675 | [
"https://github.com/lhryang/fsd"
] | https://huggingface.co/papers/2305.12675 | 0 | 0 | 0 | 6 | 1 | [] | [] | [] |
https://aclanthology.org/2024.lrec-main.48.bib | https://aclanthology.org/2024.lrec-main.48/ | @inproceedings{inadumi-etal-2024-gaze,
title = "A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous {J}apanese Questions",
author = "Inadumi, Shun and
Kawano, Seiya and
Yuguchi, Akishige and
Kawanishi, Yasutomo and
Yoshino, Koichiro",
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.48",
pages = "558--571",
abstract = "Situated conversations, which refer to visual information as visual question answering (VQA), often contain ambiguities caused by reliance on directive information. This problem is exacerbated because some languages, such as Japanese, often omit subjective or objective terms. Such ambiguities in questions are often clarified by the contexts in conversational situations, such as joint attention with a user or user gaze information. In this study, we propose the Gaze-grounded VQA dataset (GazeVQA) that clarifies ambiguous questions using gaze information by focusing on a clarification process complemented by gaze information. We also propose a method that utilizes gaze target estimation results to improve the accuracy of GazeVQA tasks. Our experimental results showed that the proposed method improved the performance in some cases of a VQA system on GazeVQA and identified some typical problems of GazeVQA tasks that need to be improved.",
}
| Situated conversations, which refer to visual information as visual question answering (VQA), often contain ambiguities caused by reliance on directive information. This problem is exacerbated because some languages, such as Japanese, often omit subjective or objective terms. Such ambiguities in questions are often clarified by the contexts in conversational situations, such as joint attention with a user or user gaze information. In this study, we propose the Gaze-grounded VQA dataset (GazeVQA) that clarifies ambiguous questions using gaze information by focusing on a clarification process complemented by gaze information. We also propose a method that utilizes gaze target estimation results to improve the accuracy of GazeVQA tasks. Our experimental results showed that the proposed method improved the performance in some cases of a VQA system on GazeVQA and identified some typical problems of GazeVQA tasks that need to be improved. | [
"Inadumi, Shun",
"Kawano, Seiya",
"Yuguchi, Akishige",
"Kawanishi, Yasutomo",
"Yoshino, Koichiro"
] | A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions | lrec-main.48 | Poster | 2403.17545 | [
"https://github.com/riken-grp/gazevqa"
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
|
https://aclanthology.org/2024.lrec-main.49.bib | https://aclanthology.org/2024.lrec-main.49/ | @inproceedings{fung-etal-2024-agenda,
title = "Agenda-Driven Question Generation: A Case Study in the Courtroom Domain",
author = "Fung, Yi and
Kumar, Anoop and
Galstyan, Aram and
Ji, Heng and
Natarajan, Prem",
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.49",
pages = "572--583",
abstract = "This paper introduces a novel problem of automated question generation for courtroom examinations, CourtQG. While question generation has been studied in domains such as educational testing and product description, CourtQG poses several unique challenges owing to its non-cooperative and agenda-driven nature. Specifically, not only the generated questions need to be relevant to the case and underlying context, they also have to achieve certain objectives such as challenging the opponent{'}s arguments and/or revealing potential inconsistencies in their answers. We propose to leverage large language models (LLM) for CourtQG by fine-tuning them on two auxiliary tasks, agenda explanation (i.e., uncovering the underlying intents) and question type prediction. We additionally propose cold-start generation of questions from background documents without relying on examination history. We construct a dataset to evaluate our proposed method and show that it generates better questions according to standard metrics when compared to several baselines.",
}
| This paper introduces a novel problem of automated question generation for courtroom examinations, CourtQG. While question generation has been studied in domains such as educational testing and product description, CourtQG poses several unique challenges owing to its non-cooperative and agenda-driven nature. Specifically, not only the generated questions need to be relevant to the case and underlying context, they also have to achieve certain objectives such as challenging the opponent{'}s arguments and/or revealing potential inconsistencies in their answers. We propose to leverage large language models (LLM) for CourtQG by fine-tuning them on two auxiliary tasks, agenda explanation (i.e., uncovering the underlying intents) and question type prediction. We additionally propose cold-start generation of questions from background documents without relying on examination history. We construct a dataset to evaluate our proposed method and show that it generates better questions according to standard metrics when compared to several baselines. | [
"Fung, Yi",
"Kumar, Anoop",
"Galstyan, Aram",
"Ji, Heng",
"Natarajan, Prem"
] | Agenda-Driven Question Generation: A Case Study in the Courtroom Domain | lrec-main.49 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.50.bib | https://aclanthology.org/2024.lrec-main.50/ | @inproceedings{zhao-penn-2024-generative,
title = "A Generative Model for {L}ambek Categorial Sequents",
author = "Zhao, Jinman and
Penn, Gerald",
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.50",
pages = "584--593",
abstract = "In this work, we introduce a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents. We also introduce a simple method to numerically estimate the model{'}s parameters from an annotated corpus. Then we compare our model with probabilistic context-free grammars (PCFGs) and show that PLC+ simultaneously assigns a higher probability to a common corpus, and has greater coverage.",
}
| In this work, we introduce a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents. We also introduce a simple method to numerically estimate the model{'}s parameters from an annotated corpus. Then we compare our model with probabilistic context-free grammars (PCFGs) and show that PLC+ simultaneously assigns a higher probability to a common corpus, and has greater coverage. | [
"Zhao, Jinman",
"Penn, Gerald"
] | A Generative Model for Lambek Categorial Sequents | lrec-main.50 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.51.bib | https://aclanthology.org/2024.lrec-main.51/ | @inproceedings{buzato-cunha-2024-agent,
title = "Agent-based Modeling of Language Change in a Small-world Network",
author = "Buzato, Dalmo and
Cunha, Evandro",
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.51",
pages = "594--599",
abstract = "Language change has been the subject of numerous studies in linguistics. However, due to the dynamic and complex nature of this phenomenon, and to the difficulty of obtaining extensive real data of language in use, some of its aspects remain obscure. In recent years, nonetheless, research has used computational modeling to simulate features related to variation, change, propagation, and evolution of languages in speech communities, finding compelling results. In this article, agent-based modeling and simulation is used to study language change. Drawing on previous studies, a speech community was modeled using Zachary{'}s karate club network, a well-established small-world network model in the field of complex systems. Idiolects were assigned through numerical values for each agent. The results demonstrate that the centrality of each agent in the network, interpreted as social prestige, appears to be a factor influencing change. Additionally, the nature of idiolects also seems to impact the spread of linguistic variants in the language change process. These findings complement the theoretical understanding of the language change phenomenon with new simulation data and provide new avenues for research.",
}
| Language change has been the subject of numerous studies in linguistics. However, due to the dynamic and complex nature of this phenomenon, and to the difficulty of obtaining extensive real data of language in use, some of its aspects remain obscure. In recent years, nonetheless, research has used computational modeling to simulate features related to variation, change, propagation, and evolution of languages in speech communities, finding compelling results. In this article, agent-based modeling and simulation is used to study language change. Drawing on previous studies, a speech community was modeled using Zachary{'}s karate club network, a well-established small-world network model in the field of complex systems. Idiolects were assigned through numerical values for each agent. The results demonstrate that the centrality of each agent in the network, interpreted as social prestige, appears to be a factor influencing change. Additionally, the nature of idiolects also seems to impact the spread of linguistic variants in the language change process. These findings complement the theoretical understanding of the language change phenomenon with new simulation data and provide new avenues for research. | [
"Buzato, Dalmo",
"Cunha, Ev",
"ro"
] | Agent-based Modeling of Language Change in a Small-world Network | lrec-main.51 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.52.bib | https://aclanthology.org/2024.lrec-main.52/ | @inproceedings{millour-etal-2024-agettivu,
title = "Agettivu, Aggitivu o Aghjettivu? {POS} Tagging {C}orsican Dialects",
author = "Millour, Alice and
Brasile, Lorenza and
Ghia, Alberto and
Kevers, Laurent",
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.52",
pages = "600--608",
abstract = "In this paper we present a series of experiments towards POS tagging Corsican, a less-resourced language spoken in Corsica and linguistically related to Italian. The first contribution is Corsican-POS, the first gold standard POS-tagged corpus for Corsica, composed of 500 sentences manually annotated with the Universal POS tagset. Our second contribution is a set of experiments and evaluation of POS tagging models which starts with a baseline model for Italian and is aimed at finding the best training configuration, namely in terms of the size and combination strategy of the existing raw and annotated resources. These experiments result in (i) the first POS tagger for Corsican, reaching an accuracy of 93.38{\%}, (ii) a quantification of the gain provided by the use of each available resource. We find that the optimal configuration uses Italian word embeddings further specialized with Corsican embeddings and trained on the largest gold corpus for Corsican available so far.",
}
| In this paper we present a series of experiments towards POS tagging Corsican, a less-resourced language spoken in Corsica and linguistically related to Italian. The first contribution is Corsican-POS, the first gold standard POS-tagged corpus for Corsica, composed of 500 sentences manually annotated with the Universal POS tagset. Our second contribution is a set of experiments and evaluation of POS tagging models which starts with a baseline model for Italian and is aimed at finding the best training configuration, namely in terms of the size and combination strategy of the existing raw and annotated resources. These experiments result in (i) the first POS tagger for Corsican, reaching an accuracy of 93.38{\%}, (ii) a quantification of the gain provided by the use of each available resource. We find that the optimal configuration uses Italian word embeddings further specialized with Corsican embeddings and trained on the largest gold corpus for Corsican available so far. | [
"Millour, Alice",
"Brasile, Lorenza",
"Ghia, Alberto",
"Kevers, Laurent"
] | Agettivu, Aggitivu o Aghjettivu? POS Tagging Corsican Dialects | lrec-main.52 | Poster | [
""
] | -1 | -1 | -1 | -1 | 0 | [] | [] | [] |
||
https://aclanthology.org/2024.lrec-main.53.bib | https://aclanthology.org/2024.lrec-main.53/ | @inproceedings{yin-etal-2024-aggregation,
title = "Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models",
author = "Yin, Zhangyue and
Sun, Qiushi and
Guo, Qipeng and
Zeng, Zhiyuan and
Li, Xiaonan and
Sun, Tianxiang and
Chang, Cheng and
Cheng, Qinyuan and
Wang, Ding and
Mou, Xiaofeng and
Qiu, Xipeng and
Huang, Xuanjing",
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.53",
pages = "609--625",
abstract = "Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances the reasoning performance of LLMs by sampling multiple reasoning chains and ensembling based on the answer frequency. However, this approach fails in scenarios where the correct answers are in the minority. We identify this as a primary factor constraining the reasoning capabilities of LLMs, a limitation that cannot be resolved solely based on the predicted answers. To address this shortcoming, we introduce a hierarchical reasoning aggregation framework AoR (Aggregation of Reasoning), which selects answers based on the evaluation of reasoning chains. Additionally, AoR incorporates dynamic sampling, adjusting the number of reasoning chains in accordance with the complexity of the task. Experimental results on a series of complex reasoning tasks show that AoR outperforms prominent ensemble methods. Further analysis reveals that AoR not only adapts various LLMs but also achieves a superior performance ceiling when compared to current methods.",
}
| Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances the reasoning performance of LLMs by sampling multiple reasoning chains and ensembling based on the answer frequency. However, this approach fails in scenarios where the correct answers are in the minority. We identify this as a primary factor constraining the reasoning capabilities of LLMs, a limitation that cannot be resolved solely based on the predicted answers. To address this shortcoming, we introduce a hierarchical reasoning aggregation framework AoR (Aggregation of Reasoning), which selects answers based on the evaluation of reasoning chains. Additionally, AoR incorporates dynamic sampling, adjusting the number of reasoning chains in accordance with the complexity of the task. Experimental results on a series of complex reasoning tasks show that AoR outperforms prominent ensemble methods. Further analysis reveals that AoR not only adapts various LLMs but also achieves a superior performance ceiling when compared to current methods. | [
"Yin, Zhangyue",
"Sun, Qiushi",
"Guo, Qipeng",
"Zeng, Zhiyuan",
"Li, Xiaonan",
"Sun, Tianxiang",
"Chang, Cheng",
"Cheng, Qinyuan",
"Wang, Ding",
"Mou, Xiaofeng",
"Qiu, Xipeng",
"Huang, Xuanjing"
] | Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models | lrec-main.53 | Poster | 2405.12939 | [
"https://github.com/yinzhangyue/AoR"
] | https://huggingface.co/papers/2405.12939 | 1 | 1 | 0 | 12 | 1 | [] | [] | [] |
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