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Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query. | <clarity> Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query. | It leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query. | clarity | 2005.03975 | 1 |
Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query. | <coherence> Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from the existing literature given a query. | Our system leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from existing literature given a query. | coherence | 2005.03975 | 1 |
In this paper, we describe our CAiRE-COVID system architecture and methodology for building the system. To bootstrap the further study, the code for our system is available at URL | <meaning-changed> In this paper, we describe our CAiRE-COVID system architecture and methodology for building the system. To bootstrap the further study, the code for our system is available at URL | Our system has been awarded as winner for one of the tasks in CORD-19 Kaggle Challenge. We also launched our CAiRE-COVID website for broader use. The code for our system is available at URL | meaning-changed | 2005.03975 | 1 |
To bootstrap the further study, the code for our system is available at URL | <clarity> To bootstrap the further study, the code for our system is available at URL | To bootstrap the further study, the code for our system is also open-sourced to bootstrap further study. | clarity | 2005.03975 | 1 |
The outbreak of COVID-19 raises attention from the researchers from various communities. | <meaning-changed> The outbreak of COVID-19 raises attention from the researchers from various communities. | We present CAiRE-COVID, a real-time question answering (QA) and multi-document summarization system, which won one of the 10 tasks in the Kaggle COVID-19 raises attention from the researchers from various communities. | meaning-changed | 2005.03975 | 2 |
The outbreak of COVID-19 raises attention from the researchers from various communities. While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. | <meaning-changed> The outbreak of COVID-19 raises attention from the researchers from various communities. While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. | The outbreak of COVID-19 Open Research Dataset Challenge, judged by medical experts. Our system aims to tackle the recent challenge of mining the numerous scientific articles being published on COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. | meaning-changed | 2005.03975 | 2 |
While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. | <meaning-changed> While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. | While many scientific articles have been published , a system that can provide reliable information to COVID-19 by answering high priority questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. | meaning-changed | 2005.03975 | 2 |
While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. To address the requests, we propose our CAiRE-COVID, a neural-based system that uses open-domain question answering (QA) techniques combined with summarization techniques for mining the available scientific literature. It leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from existing literature given a query. | <clarity> While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the latest academic resources is crucial, especially for the medical community in the current time-critical race to treat patients and to find a cure for the virus. To address the requests, we propose our CAiRE-COVID, a neural-based system that uses open-domain question answering (QA) techniques combined with summarization techniques for mining the available scientific literature. It leverages the Information Retrieval (IR) system and QA models to extract relevant snippets from existing literature given a query. | While many scientific articles have been published , a system that can provide reliable information to COVID-19 related questions from the community and summarizing salient question-related information. It combines information extraction with state-of-the-art QA and query-focused multi-document summarization techniques, selecting and highlighting evidence snippets from existing literature given a query. | clarity | 2005.03975 | 2 |
Fluent summaries are also provided to help understand the content in a more efficient way. Our system has been awarded as winner for one of the tasks in CORD-19 Kaggle Challenge. We also launched our CAiRE-COVID website for broader use . | <meaning-changed> Fluent summaries are also provided to help understand the content in a more efficient way. Our system has been awarded as winner for one of the tasks in CORD-19 Kaggle Challenge. We also launched our CAiRE-COVID website for broader use . | We also propose query-focused abstractive and extractive multi-document summarization methods, to provide more relevant information related to the question. We further conduct quantitative experiments that show consistent improvements on various metrics for each module. We have launched our website CAiRE-COVID for broader use . | meaning-changed | 2005.03975 | 2 |
We also launched our CAiRE-COVID website for broader use . The code for our system is also open-sourced to bootstrap further study . | <meaning-changed> We also launched our CAiRE-COVID website for broader use . The code for our system is also open-sourced to bootstrap further study . | We also launched our CAiRE-COVID website for broader use by the medical community, and have open-sourced the code for our system is also open-sourced to bootstrap further study . | meaning-changed | 2005.03975 | 2 |
The code for our system is also open-sourced to bootstrap further study . | <clarity> The code for our system is also open-sourced to bootstrap further study . | The code for our system , to bootstrap further study . | clarity | 2005.03975 | 2 |
The code for our system is also open-sourced to bootstrap further study . | <meaning-changed> The code for our system is also open-sourced to bootstrap further study . | The code for our system is also open-sourced to bootstrap further study by other researches . | meaning-changed | 2005.03975 | 2 |
We parameterize a dialog system using a Transformer-based auto-regressive language model, which subsumes different dialog mod-ules (e.g., state tracker, dialog policy, responsegenerator ) into a single neural model. | <fluency> We parameterize a dialog system using a Transformer-based auto-regressive language model, which subsumes different dialog mod-ules (e.g., state tracker, dialog policy, responsegenerator ) into a single neural model. | We parameterize a dialog system using a Transformer-based auto-regressive language model, which subsumes different dialog modules (e.g., state tracker, dialog policy, responsegenerator ) into a single neural model. | fluency | 2005.05298 | 1 |
We parameterize a dialog system using a Transformer-based auto-regressive language model, which subsumes different dialog mod-ules (e.g., state tracker, dialog policy, responsegenerator ) into a single neural model. | <fluency> We parameterize a dialog system using a Transformer-based auto-regressive language model, which subsumes different dialog mod-ules (e.g., state tracker, dialog policy, responsegenerator ) into a single neural model. | We parameterize a dialog system using a Transformer-based auto-regressive language model, which subsumes different dialog mod-ules (e.g., state tracker, dialog policy, response generator ) into a single neural model. | fluency | 2005.05298 | 1 |
Our experiments demonstrate that (i) SOLOIST creates new state-of-the-art results on two well-known benchmarks, CamRest and MultiWOZ, (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost. | <clarity> Our experiments demonstrate that (i) SOLOIST creates new state-of-the-art results on two well-known benchmarks, CamRest and MultiWOZ, (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost. | Our experiments demonstrate that (i) SOLOIST creates new state-of-the-art results on two well-known benchmarks, CamRest and MultiWOZ, (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those developed by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost. | clarity | 2005.05298 | 1 |
The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets). | <meaning-changed> The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets). | The dataset is diverse (covers 268 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets). | meaning-changed | 2005.06012 | 1 |
The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets). | <meaning-changed> The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets). | The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 100+ languages), and has a significant number of location-tagged tweets ( ~32M tweets). | meaning-changed | 2005.06012 | 1 |
The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets). | <meaning-changed> The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( ~32M tweets). | The dataset is diverse (covers 234 countries), longitudinal (goes as back as 2007), multilingual (comes in 65 languages), and has a significant number of location-tagged tweets ( 169M tweets). | meaning-changed | 2005.06012 | 1 |
We release tweet IDs from the dataset , hoping it will be useful for studying various phenomena related to the ongoing pandemic and accelerating viable solutions to associated problems. | <meaning-changed> We release tweet IDs from the dataset , hoping it will be useful for studying various phenomena related to the ongoing pandemic and accelerating viable solutions to associated problems. | We release tweet IDs from the dataset . We also develop and release a powerful model (acc=94\%) | meaning-changed | 2005.06012 | 1 |
ROUGE is the de facto criterion for summarization research. However, its two major drawbackslimit the research and application of automated summarization systems . | <clarity> ROUGE is the de facto criterion for summarization research. However, its two major drawbackslimit the research and application of automated summarization systems . | Canonical automatic summary evaluation metrics, such as ROUGE, suffer from two drawbacks . | clarity | 2005.06377 | 1 |
First, ROUGE favors lexical similarity instead of semantic similarity , making it especially unfit for abstractive summarization . | <clarity> First, ROUGE favors lexical similarity instead of semantic similarity , making it especially unfit for abstractive summarization . | First, semantic similarity and linguistic quality are not captured well . | clarity | 2005.06377 | 1 |
Second, ROUGE cannot function without a reference summary, which is expensive or impossible to obtain in many cases . | <coherence> Second, ROUGE cannot function without a reference summary, which is expensive or impossible to obtain in many cases . | Second, a reference summary, which is expensive or impossible to obtain in many cases . | coherence | 2005.06377 | 1 |
Second, ROUGE cannot function without a reference summary, which is expensive or impossible to obtain in many cases . Therefore , we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. | <meaning-changed> Second, ROUGE cannot function without a reference summary, which is expensive or impossible to obtain in many cases . Therefore , we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. | Second, ROUGE cannot function without a reference summary, which is expensive or impossible to obtain in many cases , is needed. Existing efforts to address the two drawbacks are done separately and have limitations. To holistically address them , we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. | meaning-changed | 2005.06377 | 1 |
Therefore , we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. | <clarity> Therefore , we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. | Therefore , we introduce an end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. | clarity | 2005.06377 | 1 |
Therefore , we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. | <clarity> Therefore , we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. | Therefore , we introduce a new end-to-end approach for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. | clarity | 2005.06377 | 1 |
Therefore , we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. Models trained in our framework can evaluate a summary directly against the input document , without the need of a reference summary . | <meaning-changed> Therefore , we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning. Models trained in our framework can evaluate a summary directly against the input document , without the need of a reference summary . | Therefore , we introduce a new end-to-end metric system for summary quality assessment by leveraging sentence or document embedding and introducing two negative sampling approaches to create training data for this supervised approach . | meaning-changed | 2005.06377 | 1 |
The proposed approach exhibits very promising results on gold-standard datasets and suggests its great potential to future summarization research. | <clarity> The proposed approach exhibits very promising results on gold-standard datasets and suggests its great potential to future summarization research. | The proposed approach exhibits promising results on gold-standard datasets and suggests its great potential to future summarization research. | clarity | 2005.06377 | 1 |
The proposed approach exhibits very promising results on gold-standard datasets and suggests its great potential to future summarization research. The scores from our models have correlation coefficients up to 0.54 with human evaluations on machine generated summaries in TAC2010. | <meaning-changed> The proposed approach exhibits very promising results on gold-standard datasets and suggests its great potential to future summarization research. The scores from our models have correlation coefficients up to 0.54 with human evaluations on machine generated summaries in TAC2010. | The proposed approach exhibits very promising results on several summarization datasets of various domains including news, legislative bills, scientific papers, and patents. When rating machine-generated summaries in TAC2010, our approach outperforms ROUGE in terms of linguistic quality, and achieves a correlation coefficient of up to 0.5702 with human evaluations on machine generated summaries in TAC2010. | meaning-changed | 2005.06377 | 1 |
The scores from our models have correlation coefficients up to 0.54 with human evaluations on machine generated summaries in TAC2010. Its performance is also very close to ROUGE metrics' . | <meaning-changed> The scores from our models have correlation coefficients up to 0.54 with human evaluations on machine generated summaries in TAC2010. Its performance is also very close to ROUGE metrics' . | The scores from our models have correlation coefficients up to 0.54 with human evaluations in terms of modified pyramid scores. We hope our approach can facilitate summarization research or applications when reference summaries are infeasible or costly to obtain, or when linguistic quality is a focus . | meaning-changed | 2005.06377 | 1 |
Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . | <style> Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . | Bidirectional Encoder Representations from Transformers (BERT) models for medical specialties, such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . | style | 2005.07202 | 1 |
Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . | <fluency> Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . | Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT , have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . | fluency | 2005.07202 | 1 |
Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . | <clarity> Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . | Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in performing biomedical text mining tasks and have enabled extracting valuable information from biomedical literature . | clarity | 2005.07202 | 1 |
Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . However, we benefitted only in English because of the significant scarcity of high-quality medical documents, such as PubMed, in each language. | <clarity> Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature . However, we benefitted only in English because of the significant scarcity of high-quality medical documents, such as PubMed, in each language. | Bidirectional Encoder Representations from Transformers (BERT) models for biomedical specialties such as BioBERT and clinicalBERT have significantly improved in biomedical text-mining tasks and enabled us to extract valuable information from biomedical literature ; however, only English speakers benefit due to the significant scarcity of high-quality medical documents, such as PubMed, in each language. | clarity | 2005.07202 | 1 |
Therefore, we propose a method that realizes a high-performance BERT model by using a small corpus. | <clarity> Therefore, we propose a method that realizes a high-performance BERT model by using a small corpus. | Therefore, we propose a method to train a high-performance BERT model by using a small corpus. | clarity | 2005.07202 | 1 |
Therefore, we propose a method that realizes a high-performance BERT model by using a small corpus. | <coherence> Therefore, we propose a method that realizes a high-performance BERT model by using a small corpus. | Therefore, we propose a method that realizes a high-performance BERT model using a small corpus. | coherence | 2005.07202 | 1 |
We introduce the method to train a BERT model on a small medical corpus both in English and Japanese, respectively, and then we evaluate each of them in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical-document-classification task in Japanese, respectively. | <clarity> We introduce the method to train a BERT model on a small medical corpus both in English and Japanese, respectively, and then we evaluate each of them in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical-document-classification task in Japanese, respectively. | We introduce the method to train a BERT model on a small medical corpus both in English and in Japanese, and we present the evaluation of each model in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical-document-classification task in Japanese, respectively. | clarity | 2005.07202 | 1 |
We introduce the method to train a BERT model on a small medical corpus both in English and Japanese, respectively, and then we evaluate each of them in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical-document-classification task in Japanese, respectively. | <clarity> We introduce the method to train a BERT model on a small medical corpus both in English and Japanese, respectively, and then we evaluate each of them in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical-document-classification task in Japanese, respectively. | We introduce the method to train a BERT model on a small medical corpus both in English and Japanese, respectively, and then we evaluate each of them in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively. | clarity | 2005.07202 | 1 |
After confirming their satisfactory performances, we apply our method to develop a model that outperforms the pre-existing models. | <fluency> After confirming their satisfactory performances, we apply our method to develop a model that outperforms the pre-existing models. | After confirming their satisfactory performances, we applied our method to develop a model that outperforms the pre-existing models. | fluency | 2005.07202 | 1 |
After confirming their satisfactory performances, we apply our method to develop a model that outperforms the pre-existing models. | <clarity> After confirming their satisfactory performances, we apply our method to develop a model that outperforms the pre-existing models. | After confirming their satisfactory performances, we apply our method to develop a model comparable to the publicly available models. OuBioBERT, short for | clarity | 2005.07202 | 1 |
Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University (ouBioBERT) achieves the best scores on 7 of the 10 datasets in terms of the BLUE benchmark. | <clarity> Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University (ouBioBERT) achieves the best scores on 7 of the 10 datasets in terms of the BLUE benchmark. | Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University , achieved the best score in terms of the BLUE benchmark. | clarity | 2005.07202 | 1 |
The total score is 1.0 points above that of BioBERT . | <meaning-changed> The total score is 1.0 points above that of BioBERT . | The total score is 1.1 points above that of BioBERT . | meaning-changed | 2005.07202 | 1 |
The total score is 1.0 points above that of BioBERT . | <meaning-changed> The total score is 1.0 points above that of BioBERT . | The total score is 1.0 points above that of BioBERT and 0.3 points above that of the ablated model trained without our proposed method. This proposed technique is an effective approach to develop localized medical BERT models and to enhance domain-specific models in the biomedical domain . | meaning-changed | 2005.07202 | 1 |
Bidirectional Encoder Representations from Transformers (BERT)models for medical specialties , such as BioBERT and clinicalBERT, have significantly improved in performing biomedical text mining tasks and have enabled extracting valuable information from biomedical literature ; | <meaning-changed> Bidirectional Encoder Representations from Transformers (BERT)models for medical specialties , such as BioBERT and clinicalBERT, have significantly improved in performing biomedical text mining tasks and have enabled extracting valuable information from biomedical literature ; | Pre-training large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing (NLP). With the introduction of transformer-based language models , such as BioBERT and clinicalBERT, have significantly improved in performing biomedical text mining tasks and have enabled extracting valuable information from biomedical literature ; | meaning-changed | 2005.07202 | 2 |
Bidirectional Encoder Representations from Transformers (BERT)models for medical specialties , such as BioBERT and clinicalBERT, have significantly improved in performing biomedical text mining tasks and have enabled extracting valuable information from biomedical literature ; | <clarity> Bidirectional Encoder Representations from Transformers (BERT)models for medical specialties , such as BioBERT and clinicalBERT, have significantly improved in performing biomedical text mining tasks and have enabled extracting valuable information from biomedical literature ; | Bidirectional Encoder Representations from Transformers (BERT)models for medical specialties , such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from a free text by NLP has significantly improved for both the general domain and medical domain ; | clarity | 2005.07202 | 2 |
however, only English speakers benefit due to the significant scarcity of high-quality medical documents, such as PubMed, in each language. Therefore, we propose a method to train a high-performance BERT model using a small corpus . We introduce the method to train a BERT model on a small medical corpus both in English and in Japanese , and we present the evaluation of each model in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively. | <meaning-changed> however, only English speakers benefit due to the significant scarcity of high-quality medical documents, such as PubMed, in each language. Therefore, we propose a method to train a high-performance BERT model using a small corpus . We introduce the method to train a BERT model on a small medical corpus both in English and in Japanese , and we present the evaluation of each model in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively. | however, it is difficult to train specific BERT models that perform well for domains in which there are few publicly available databases of high quality and large size. We hypothesized that this problem can be addressed by up-sampling a domain-specific corpus and using it for pre-training with a larger corpus in a balanced manner. Our proposed method consists of a single intervention with one option: simultaneous pre-training after up-sampling and amplified vocabulary. We conducted three experiments and evaluated the resulting products. We confirmed that our Japanese medical BERT outperformed conventional baselines and the other BERT models in terms of the medical document classification task and that our English BERT pre-trained using both the general and medical-domain corpora performed sufficiently well for practical use in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively. | meaning-changed | 2005.07202 | 2 |
We introduce the method to train a BERT model on a small medical corpus both in English and in Japanese , and we present the evaluation of each model in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively. After confirming their satisfactory performances, we applied our method to develop a modelcomparable to the publicly available models. OuBioBERT, short for Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University, achieved the best score in terms of the BLUE benchmark . | <coherence> We introduce the method to train a BERT model on a small medical corpus both in English and in Japanese , and we present the evaluation of each model in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively. After confirming their satisfactory performances, we applied our method to develop a modelcomparable to the publicly available models. OuBioBERT, short for Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University, achieved the best score in terms of the BLUE benchmark . | We introduce the method to train a BERT model on a small medical corpus both in English and in Japanese , and we present the evaluation of each model in terms of the biomedical language understanding evaluation (BLUE) benchmark . Moreover, our enhanced biomedical BERT model, in which clinical notes were not used during pre-training, showed that both the clinical and biomedical scores of the BLUE benchmark . | coherence | 2005.07202 | 2 |
OuBioBERT, short for Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University, achieved the best score in terms of the BLUE benchmark . The total score is 1.1 points above that of BioBERT and 0.3 points above that of the ablated model trained without our proposed method. | <clarity> OuBioBERT, short for Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University, achieved the best score in terms of the BLUE benchmark . The total score is 1.1 points above that of BioBERT and 0.3 points above that of the ablated model trained without our proposed method. | OuBioBERT, short for Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University, achieved the best score in terms of the BLUE benchmark were 0.3 points above that of the ablated model trained without our proposed method. | clarity | 2005.07202 | 2 |
The total score is 1.1 points above that of BioBERT and 0.3 points above that of the ablated model trained without our proposed method. | <fluency> The total score is 1.1 points above that of BioBERT and 0.3 points above that of the ablated model trained without our proposed method. | The total score is 1.1 points above that of BioBERT and 0.3 points above that of the ablation model trained without our proposed method. | fluency | 2005.07202 | 2 |
This proposed technique is an effective approach to develop localized medical BERT models and to enhance domain-specific models in the biomedical domain . | <clarity> This proposed technique is an effective approach to develop localized medical BERT models and to enhance domain-specific models in the biomedical domain . | Well-balanced pre-training by up-sampling instances derived from a corpus appropriate for the target task allows us to construct a high-performance BERT model . | clarity | 2005.07202 | 2 |
Word embeddings represent words in a numeric space in such a way that semantic relations between words are encoded as distances and directions in the vector space. | <clarity> Word embeddings represent words in a numeric space in such a way that semantic relations between words are encoded as distances and directions in the vector space. | Word embeddings represent words in a numeric space so that semantic relations between words are encoded as distances and directions in the vector space. | clarity | 2005.07456 | 1 |
Word embeddings represent words in a numeric space in such a way that semantic relations between words are encoded as distances and directions in the vector space. | <clarity> Word embeddings represent words in a numeric space in such a way that semantic relations between words are encoded as distances and directions in the vector space. | Word embeddings represent words in a numeric space in such a way that semantic relations between words are represented as distances and directions in the vector space. | clarity | 2005.07456 | 1 |
Cross-lingual word embeddings map words from one language to the vector space of another language, or words from multiple languages to the same vector space where similar words are aligned. | <clarity> Cross-lingual word embeddings map words from one language to the vector space of another language, or words from multiple languages to the same vector space where similar words are aligned. | Cross-lingual word embeddings transform vector spaces of different languages so that similar words are aligned. | clarity | 2005.07456 | 1 |
Cross-lingual embeddings can be used to transfer machine learning models between languages and thereby compensate for insufficient data in less-resourced languages. | <meaning-changed> Cross-lingual embeddings can be used to transfer machine learning models between languages and thereby compensate for insufficient data in less-resourced languages. | This is done by constructing a mapping between vector spaces of two languages or learning a joint vector space for multiple languages. Cross-lingual embeddings can be used to transfer machine learning models between languages and thereby compensate for insufficient data in less-resourced languages. | meaning-changed | 2005.07456 | 1 |
Cross-lingual embeddings can be used to transfer machine learning models between languages and thereby compensate for insufficient data in less-resourced languages. | <clarity> Cross-lingual embeddings can be used to transfer machine learning models between languages and thereby compensate for insufficient data in less-resourced languages. | Cross-lingual embeddings can be used to transfer machine learning models between languages , thereby compensating for insufficient data in less-resourced languages. | clarity | 2005.07456 | 1 |
We focus on two transfer mechanisms using the joint numerical space for many languages as implemented in the LASER library : the transfer of trained models, and expansion of training sets with instances from other languages . | <meaning-changed> We focus on two transfer mechanisms using the joint numerical space for many languages as implemented in the LASER library : the transfer of trained models, and expansion of training sets with instances from other languages . | We focus on two transfer mechanisms that recently show superior transfer performance. The first mechanism uses the trained models whose input is the joint numerical space for many languages as implemented in the LASER library : the transfer of trained models, and expansion of training sets with instances from other languages . | meaning-changed | 2005.07456 | 1 |
We focus on two transfer mechanisms using the joint numerical space for many languages as implemented in the LASER library : the transfer of trained models, and expansion of training sets with instances from other languages . | <meaning-changed> We focus on two transfer mechanisms using the joint numerical space for many languages as implemented in the LASER library : the transfer of trained models, and expansion of training sets with instances from other languages . | We focus on two transfer mechanisms using the joint numerical space for many languages as implemented in the LASER library . The second mechanism uses large pretrained multilingual BERT language models . | meaning-changed | 2005.07456 | 1 |
Our experiments show that the transfer of models between similar languages is sensible, while dataset expansion did not increase the predictive performance . | <meaning-changed> Our experiments show that the transfer of models between similar languages is sensible, while dataset expansion did not increase the predictive performance . | Our experiments show that the transfer of models between similar languages is sensible, even with no target language data. The performance of cross-lingual models obtained with the multilingual BERT and LASER library is comparable, and the differences are language-dependent. The transfer with CroSloEngual BERT, pretrained on only three languages, is superior on these and some closely related languages . | meaning-changed | 2005.07456 | 1 |
The first stage is to delete attribute markers of a sentence directly through the classifier. | <fluency> The first stage is to delete attribute markers of a sentence directly through the classifier. | The first stage is to delete attribute markers of a sentence directly through a classifier. | fluency | 2005.12086 | 1 |
The second stage is to generate the transferred sentence by combining the content tokens and the target style. | <fluency> The second stage is to generate the transferred sentence by combining the content tokens and the target style. | The second stage is to generate a transferred sentence by combining the content tokens and the target style. | fluency | 2005.12086 | 1 |
We evaluate systems on two benchmark datasets . | <clarity> We evaluate systems on two benchmark datasets . | We experiment on two benchmark datasets . | clarity | 2005.12086 | 1 |
We evaluate systems on two benchmark datasets . Transferred sentences are evaluated in terms of context, style, fluency, and semantic. | <coherence> We evaluate systems on two benchmark datasets . Transferred sentences are evaluated in terms of context, style, fluency, and semantic. | We evaluate systems on two benchmark datasets and evaluate context, style, fluency, and semantic. | coherence | 2005.12086 | 1 |
These evaluation metricsare used to determine a stable system. Only robust systems in all evaluation metrics are suitable for use in real applications. | <clarity> These evaluation metricsare used to determine a stable system. Only robust systems in all evaluation metrics are suitable for use in real applications. | It is difficult to select the best system using only these automatic metrics, but it is possible to select stable systems. We consider only robust systems in all evaluation metrics are suitable for use in real applications. | clarity | 2005.12086 | 1 |
Only robust systems in all evaluation metrics are suitable for use in real applications. | <clarity> Only robust systems in all evaluation metrics are suitable for use in real applications. | Only robust systems in all automatic evaluation metrics to be the minimum conditions that can be used in real applications. | clarity | 2005.12086 | 1 |
Many previous systems are difficult to use in certain situations because they are unstable in some evaluation metrics. | <clarity> Many previous systems are difficult to use in certain situations because they are unstable in some evaluation metrics. | Many previous systems are difficult to use in certain situations because performance is significantly lower in several evaluation metrics. | clarity | 2005.12086 | 1 |
However, our system is stable in all evaluation metrics and has results comparable to other models. | <meaning-changed> However, our system is stable in all evaluation metrics and has results comparable to other models. | However, our system is stable in all automatic evaluation metrics and has results comparable to other models. | meaning-changed | 2005.12086 | 1 |
However, our system is stable in all evaluation metrics and has results comparable to other models. | <meaning-changed> However, our system is stable in all evaluation metrics and has results comparable to other models. | However, our system is stable in all evaluation metrics and has results comparable to other models. Also, we compare the performance results of our system and the unstable system through human evaluation. Our code and data are available at the URL | meaning-changed | 2005.12086 | 1 |
We evaluate CERT on three language understanding tasks : CoLA, RTE, and QNLI. CERT outperforms BERT significantly. | <meaning-changed> We evaluate CERT on three language understanding tasks : CoLA, RTE, and QNLI. CERT outperforms BERT significantly. | We evaluate CERT on 11 natural language understanding tasks : CoLA, RTE, and QNLI. CERT outperforms BERT significantly. | meaning-changed | 2005.12766 | 1 |
We evaluate CERT on three language understanding tasks : CoLA, RTE, and QNLI. CERT outperforms BERT significantly. | <meaning-changed> We evaluate CERT on three language understanding tasks : CoLA, RTE, and QNLI. CERT outperforms BERT significantly. | We evaluate CERT on three language understanding tasks in the GLUE benchmark where CERT outperforms BERT on 7 tasks, achieves the same performance as BERT on 2 tasks, and performs worse than BERT on 2 tasks. On the averaged score of the 11 tasks, CERT outperforms BERT significantly. | meaning-changed | 2005.12766 | 1 |
We evaluate CERT on three language understanding tasks : CoLA, RTE, and QNLI. CERT outperforms BERT significantly. | <meaning-changed> We evaluate CERT on three language understanding tasks : CoLA, RTE, and QNLI. CERT outperforms BERT significantly. | We evaluate CERT on three language understanding tasks : CoLA, RTE, and QNLI. CERT outperforms BERT . The data and code are available at URL | meaning-changed | 2005.12766 | 1 |
Few resources represent implicit roles for natural language understanding , and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. | <meaning-changed> Few resources represent implicit roles for natural language understanding , and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. | Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for natural language understanding , and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. | meaning-changed | 2005.12889 | 1 |
Few resources represent implicit roles for natural language understanding , and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. | <style> Few resources represent implicit roles for natural language understanding , and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. | Few resources represent implicit roles for NLU , and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. | style | 2005.12889 | 1 |
In this paper , we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer (Abend and Rappoport, 2013). | <clarity> In this paper , we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer (Abend and Rappoport, 2013). | This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer (Abend and Rappoport, 2013). | clarity | 2005.12889 | 1 |
In this paper , we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer (Abend and Rappoport, 2013). Our design aligns with O'Gorman (2019)'s implicit role interpretation in a linguistic and computational model. | <clarity> In this paper , we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer (Abend and Rappoport, 2013). Our design aligns with O'Gorman (2019)'s implicit role interpretation in a linguistic and computational model. | In this paper , we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer . | clarity | 2005.12889 | 1 |
The proposed implicit argument categorisation set consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. | <meaning-changed> The proposed implicit argument categorisation set consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. | The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. | meaning-changed | 2005.12889 | 1 |
We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . | <clarity> We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . | We exemplify our design by revisiting part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . | clarity | 2005.12889 | 1 |
We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . | <fluency> We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . | We corroborate the theory by reviewing and refining part of the UCCA EWT corpus , providing a new dataset alongside comparative analysis with other schemes . | fluency | 2005.12889 | 1 |
We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . | <meaning-changed> We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . | We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes . | meaning-changed | 2005.12889 | 1 |
We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . It is anticipated that our study will inspire tailored design of implicit role annotation in other meaning representation frameworks, and stimulate research in relevant fields, such as coreference resolution and question answering . | <clarity> We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . It is anticipated that our study will inspire tailored design of implicit role annotation in other meaning representation frameworks, and stimulate research in relevant fields, such as coreference resolution and question answering . | We corroborate the theory by reviewing and refining part of the UCCA EWT corpus and providing a new dataset alongside comparative analysis with other schemes . | clarity | 2005.12889 | 1 |
One of the most crucial challenges in questionanswering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer(QA) pairs for a target text domain with human annotation. | <fluency> One of the most crucial challenges in questionanswering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer(QA) pairs for a target text domain with human annotation. | One of the most crucial challenges in question answering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer(QA) pairs for a target text domain with human annotation. | fluency | 2005.13837 | 1 |
An alternative approach totackle the problem is to use automatically generated QA pairs from either the problem context or from large amount of unstructured texts(e.g. Wikipedia). | <fluency> An alternative approach totackle the problem is to use automatically generated QA pairs from either the problem context or from large amount of unstructured texts(e.g. Wikipedia). | An alternative approach to tackle the problem is to use automatically generated QA pairs from either the problem context or from large amount of unstructured texts(e.g. Wikipedia). | fluency | 2005.13837 | 1 |
In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QApairs to ensure their consistency. | <fluency> In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QApairs to ensure their consistency. | In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QApairs to ensure their consistency. | fluency | 2005.13837 | 1 |
In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QApairs to ensure their consistency. | <fluency> In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QApairs to ensure their consistency. | In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizingthe mutual information between generated QA pairs to ensure their consistency. | fluency | 2005.13837 | 1 |
We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | <fluency> We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | We validate our Information Maximizing Hierarchical Conditional Variational AutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | fluency | 2005.13837 | 1 |
We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | <fluency> We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets by evaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | fluency | 2005.13837 | 1 |
We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | <fluency> We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QA pairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | fluency | 2005.13837 | 1 |
We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | <fluency> We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using boththe generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | We validateourInformation MaximizingHierarchicalConditionalVariationalAutoEncoder (Info-HCVAE) on several benchmark datasets byevaluating the performance of the QA model(BERT-base) using only the generated QApairs (QA-based evaluation) or by using both the generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. | fluency | 2005.13837 | 1 |
The results showthat our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training | <fluency> The results showthat our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training | The results show that our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training | fluency | 2005.13837 | 1 |
The results showthat our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training | <fluency> The results showthat our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training | The results showthat our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training . | fluency | 2005.13837 | 1 |
In this paper we present MaChAmp, a toolkit for easy use of fine-tuning BERT-like models in multi-task settings. | <coherence> In this paper we present MaChAmp, a toolkit for easy use of fine-tuning BERT-like models in multi-task settings. | In this paper we present MaChAmp, a toolkit for easy fine-tuning BERT-like models in multi-task settings. | coherence | 2005.14672 | 1 |
In this paper we present MaChAmp, a toolkit for easy use of fine-tuning BERT-like models in multi-task settings. | <meaning-changed> In this paper we present MaChAmp, a toolkit for easy use of fine-tuning BERT-like models in multi-task settings. | In this paper we present MaChAmp, a toolkit for easy use of fine-tuning of contextualized embeddings in multi-task settings. | meaning-changed | 2005.14672 | 1 |
The benefits of MaChAmp are its flexible configuration options, and the support of a variety of NLP tasks in a uniform toolkit, from text classification to sequence labeling and dependency parsing . | <clarity> The benefits of MaChAmp are its flexible configuration options, and the support of a variety of NLP tasks in a uniform toolkit, from text classification to sequence labeling and dependency parsing . | The benefits of MaChAmp are its flexible configuration options, and the support of a variety of natural language processing tasks in a uniform toolkit, from text classification to sequence labeling and dependency parsing . | clarity | 2005.14672 | 1 |
The benefits of MaChAmp are its flexible configuration options, and the support of a variety of NLP tasks in a uniform toolkit, from text classification to sequence labeling and dependency parsing . | <meaning-changed> The benefits of MaChAmp are its flexible configuration options, and the support of a variety of NLP tasks in a uniform toolkit, from text classification to sequence labeling and dependency parsing . | The benefits of MaChAmp are its flexible configuration options, and the support of a variety of NLP tasks in a uniform toolkit, from text classification and sequence labeling to dependency parsing, masked language modeling, and text generation . | meaning-changed | 2005.14672 | 1 |
One account of the informativity effect on duration is that the acoustic details of word reduction are stored as part of a word's representation. | <meaning-changed> One account of the informativity effect on duration is that the acoustic details of word reduction are stored as part of a word's representation. | One account of the informativity effect on duration is that the acoustic details of probabilistic reduction are stored as part of a word's representation. | meaning-changed | 2005.14716 | 1 |
One account of the informativity effect on duration is that the acoustic details of word reduction are stored as part of a word's representation. | <clarity> One account of the informativity effect on duration is that the acoustic details of word reduction are stored as part of a word's representation. | One account of the informativity effect on duration is that the acoustic details of word reduction are stored as part of a word's mental representation. | clarity | 2005.14716 | 1 |
Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that lexical representation of a word includes phonetic details associated with its prosodic prominence . | <clarity> Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that lexical representation of a word includes phonetic details associated with its prosodic prominence . | Extending this interpretation, these results suggest that predictability is closely linked to prosodic prominence, and that lexical representation of a word includes phonetic details associated with its prosodic prominence . | clarity | 2005.14716 | 1 |
Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that lexical representation of a word includes phonetic details associated with its prosodic prominence . | <fluency> Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that lexical representation of a word includes phonetic details associated with its prosodic prominence . | Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that the lexical representation of a word includes phonetic details associated with its prosodic prominence . | fluency | 2005.14716 | 1 |
Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that lexical representation of a word includes phonetic details associated with its prosodic prominence . | <meaning-changed> Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that lexical representation of a word includes phonetic details associated with its prosodic prominence . | Extending this interpretation, these results suggest that informativity is closely linked to prosodic prominence, and that lexical representation of a word includes phonetic details associated with its average prosodic prominence in discourse . | meaning-changed | 2005.14716 | 1 |
We present ExplainIt, a review summarization system centered around opinion explainability: the simple notion of high-level opinions (e.g. "noisy room") being explainable by lower-level ones (e.g., "loud fridge"). | <meaning-changed> We present ExplainIt, a review summarization system centered around opinion explainability: the simple notion of high-level opinions (e.g. "noisy room") being explainable by lower-level ones (e.g., "loud fridge"). | The Web is a major resource of both factual and subjective information. While there are significant efforts URLanize factual information into knowledge bases, there is much less work URLanizing opinions, which are abundant in subjective data, into a structured format. We present ExplainIt, a review summarization system centered around opinion explainability: the simple notion of high-level opinions (e.g. "noisy room") being explainable by lower-level ones (e.g., "loud fridge"). | meaning-changed | 2006.00119 | 1 |
We present ExplainIt, a review summarization system centered around opinion explainability: the simple notion of high-level opinions (e.g. "noisy room") being explainable by lower-level ones (e.g., "loud fridge"). ExplainIt utilizes a combination of supervised and unsupervised components to mine the opinion phrasesfrom reviews URLanize them in an Opinion Causality Graph (OCG), a novel semi-structured representation which summarizes causal relations. To construct an OCG, we cluster semantically similar opinions in single nodes, thus canonicalizing opinion paraphrases, and draw directed edges between node pairs that are likely connected by a causal relation. | <clarity> We present ExplainIt, a review summarization system centered around opinion explainability: the simple notion of high-level opinions (e.g. "noisy room") being explainable by lower-level ones (e.g., "loud fridge"). ExplainIt utilizes a combination of supervised and unsupervised components to mine the opinion phrasesfrom reviews URLanize them in an Opinion Causality Graph (OCG), a novel semi-structured representation which summarizes causal relations. To construct an OCG, we cluster semantically similar opinions in single nodes, thus canonicalizing opinion paraphrases, and draw directed edges between node pairs that are likely connected by a causal relation. | We present ExplainIt, a system that extracts URLanizes opinions into an opinion graph, which are useful for downstream applications such as generating explainable review summaries and facilitating search over opinion phrases. In such graphs, a node represents a set of semantically similar opinions in single nodes, thus canonicalizing opinion paraphrases, and draw directed edges between node pairs that are likely connected by a causal relation. | clarity | 2006.00119 | 1 |
To construct an OCG, we cluster semantically similar opinions in single nodes, thus canonicalizing opinion paraphrases, and draw directed edges between node pairs that are likely connected by a causal relation. OCGs can be used to generate structured summaries at different levels of granularity and for certain aspects of interest, while simultaneously providing explanations . In this paper, we present the system's individual components and evaluate their effectiveness on their respective sub-tasks, where we report substantial improvements over baselines across two domains. Finally, we validate these results with a user study, showing that ExplainIt produces reasonable opinion explanations according to human judges . | <style> To construct an OCG, we cluster semantically similar opinions in single nodes, thus canonicalizing opinion paraphrases, and draw directed edges between node pairs that are likely connected by a causal relation. OCGs can be used to generate structured summaries at different levels of granularity and for certain aspects of interest, while simultaneously providing explanations . In this paper, we present the system's individual components and evaluate their effectiveness on their respective sub-tasks, where we report substantial improvements over baselines across two domains. Finally, we validate these results with a user study, showing that ExplainIt produces reasonable opinion explanations according to human judges . | To construct an OCG, we cluster semantically similar opinions extracted from reviews and an edge between two nodes signifies that one node explains the other. ExplainIt mines explanations in a supervised method and groups similar opinions together in a weakly supervised way before combining the clusters of opinions together with their explanation relationships into an opinion graph. We experimentally demonstrate that the explanation relationships generated in the opinion graph are of good quality and our labeled datasets for explanation mining and grouping opinions are publicly available . | style | 2006.00119 | 1 |