Proceedings of COLING. International Conference on Computational Linguistics最新文献_第2页

Reinforcement Learning with Large Action Spaces for Neural Machine Translation 基于大动作空间的神经机器翻译强化学习

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-10-06 DOI: 10.48550/arXiv.2210.03053

Asaf Yehudai, Leshem Choshen, Lior Fox, Omri Abend

引用次数: 3

Teaching Neural Module Networks to Do Arithmetic 教神经模块网络做算术

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-10-06 DOI: 10.48550/arXiv.2210.02703

Jiayi Chen, Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari

引用次数: 0

Debiasing Isn’t Enough! – on the Effectiveness of Debiasing MLMs and Their Social Biases in Downstream Tasks 消除偏见是不够的!——关于消除传销及其社会偏见在下游任务中的有效性

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-10-06 DOI: 10.48550/arXiv.2210.02938

Masahiro Kaneko, D. Bollegala, Naoaki Okazaki

引用次数: 18

Schema Encoding for Transferable Dialogue State Tracking 可转移对话状态跟踪的模式编码

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-10-05 DOI: 10.48550/arXiv.2210.02351

Hyunmin Jeon, G. G. Lee

引用次数: 0

CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations CorefDiffs:基于文档的对话中的共同参考和差异知识流

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-10-05 DOI: 10.48550/arXiv.2210.02223

Lin Xu, Qixian Zhou, Jinlan Fu, Min-Yen Kan, See-Kiong Ng

{"title":"CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations","authors":"Lin Xu, Qixian Zhou, Jinlan Fu, Min-Yen Kan, See-Kiong Ng","doi":"10.48550/arXiv.2210.02223","DOIUrl":"https://doi.org/10.48550/arXiv.2210.02223","url":null,"abstract":"Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally. For document-grounded dialog systems, the inter- and intra-document knowledge relations can be used to model such conversational flows. We develop a novel Multi-Document Co-Referential Graph (Coref-MDG) to effectively capture the inter-document relationships based on commonsense and similarity and the intra-document co-referential structures of knowledge segments within the grounding documents. We propose CorefDiffs, a Co-referential and Differential flow management method, to linearize the static Coref-MDG into conversational sequence logic. CorefDiffs performs knowledge selection by accounting for contextual graph structures and the knowledge difference sequences. CorefDiffs significantly outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. This demonstrates that the effective modeling of co-reference and knowledge difference for dialog flows are critical for transitions in document-grounded conversation.","PeriodicalId":91381,"journal":{"name":"Proceedings of COLING. International Conference on Computational Linguistics","volume":"33 1","pages":"471-484"},"PeriodicalIF":0.0,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80050100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A New Public Corpus for Clinical Section Identification: MedSecId. 一个新的临床切片识别公共语料库：MedSecId。

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-10-01

Paul Landes, Kunal Patel, Sean S Huang, Adam Webb, Barbara Di Eugenio, Cornelia Caragea

引用次数: 0

Edinburgh_UCL_Health@SMM4H'22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination. Edinburgh_UCL_Health@SMM4H'22：从手套到Flair处理与药物不良事件，药物变化和自我报告疫苗接种相关的不平衡保健语料库。

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-10-01

Imane Guellil, Jinge Wu, Honghan Wu, Tony Sun, Beatrice Alex

引用次数: 0

Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language. 基于转换器的神经非典型语言模型的性能评估。

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-10-01

Duanchen Liu, Zoey Liu, Qingyun Yang, Yujing Huang, Emily Prud'hommeaux

{"title":"Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language.","authors":"Duanchen Liu, Zoey Liu, Qingyun Yang, Yujing Huang, Emily Prud'hommeaux","doi":"","DOIUrl":"","url":null,"abstract":"Difficulties with social aspects of language are among the hallmarks of autism spectrum disorder (ASD). These communication differences are thought to contribute to the challenges that adults with ASD experience when seeking employment, underscoring the need for interventions that focus on improving areas of weakness in pragmatic and social language. In this paper, we describe a transformer-based framework for identifying linguistic features associated with social aspects of communication using a corpus of conversations between adults with and without ASD and neurotypical conversational partners produced while engaging in collaborative tasks. While our framework yields strong accuracy overall, performance is significantly worse for the language of participants with ASD, suggesting that they use a more diverse set of strategies for some social linguistic functions. These results, while showing promise for the development of automated language analysis tools to support targeted language interventions for ASD, also reveal weaknesses in the ability of large contextualized language models to model neuroatypical language.","PeriodicalId":91381,"journal":{"name":"Proceedings of COLING. International Conference on Computational Linguistics","volume":"2022 ","pages":"3412-3419"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633182/pdf/nihms-1846175.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40683834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Summarizing Patients' Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models. 使用预先训练的序列到序列模型总结医院进展记录中的患者问题。

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-10-01

Yanjun Gao, Timothy Miller, Dongfang Xu, Dmitriy Dligach, Matthew M Churpek, Majid Afshar

{"title":"Summarizing Patients' Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models.","authors":"Yanjun Gao, Timothy Miller, Dongfang Xu, Dmitriy Dligach, Matthew M Churpek, Majid Afshar","doi":"","DOIUrl":"","url":null,"abstract":"Automatically summarizing patients' main problems from daily progress notes using natural language processing methods helps to battle against information and cognitive overload in hospital settings and potentially assists providers with computerized diagnostic decision support. Problem list summarization requires a model to understand, abstract, and generate clinical documentation. In this work, we propose a new NLP task that aims to generate a list of problems in a patient's daily care plan using input from the provider's progress notes during hospitalization. We investigate the performance of T5 and BART, two state-of-the-art seq2seq transformer architectures, in solving this problem. We provide a corpus built on top of progress notes from publicly available electronic health record progress notes in the Medical Information Mart for Intensive Care (MIMIC)-III. T5 and BART are trained on general domain text, and we experiment with a data augmentation method and a domain adaptation pre-training method to increase exposure to medical vocabulary and knowledge. Evaluation methods include ROUGE, BERTScore, cosine similarity on sentence embedding, and F-score on medical concepts. Results show that T5 with domain adaptive pre-training achieves significant performance gains compared to a rule-based system and general domain pre-trained language models, indicating a promising direction for tackling the problem summarization task.","PeriodicalId":91381,"journal":{"name":"Proceedings of COLING. International Conference on Computational Linguistics","volume":"2022 ","pages":"2979-2991"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581107/pdf/nihms-1840629.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40648441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision 基于知识基础和语义自我监督的医学问题理解与回答

Proceedings of COLING. International Conference on Computational Linguistics Pub Date : 2022-09-30 DOI: 10.48550/arXiv.2209.15301

Khalil Mrini, Harpreet Singh, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, E. Farcas, Ndapandula Nakashole

{"title":"Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision","authors":"Khalil Mrini, Harpreet Singh, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, E. Farcas, Ndapandula Nakashole","doi":"10.48550/arXiv.2209.15301","DOIUrl":"https://doi.org/10.48550/arXiv.2209.15301","url":null,"abstract":"Current medical question answering systems have difficulty processing long, detailed and informally worded questions submitted by patients, called Consumer Health Questions (CHQs). To address this issue, we introduce a medical question understanding and answering system with knowledge grounding and semantic self-supervision. Our system is a pipeline that first summarizes a long, medical, user-written question, using a supervised summarization loss. Then, our system performs a two-step retrieval to return answers. The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document. In the absence of labels for question matching or answer relevance, we design 3 novel, self-supervised and semantically-guided losses. We evaluate our model against two strong retrieval-based question answering baselines. Evaluators ask their own questions and rate the answers retrieved by our baselines and own system according to their relevance. They find that our system retrieves more relevant answers, while achieving speeds 20 times faster. Our self-supervised losses also help the summarizer achieve higher scores in ROUGE, as well as in human evaluation metrics.","PeriodicalId":91381,"journal":{"name":"Proceedings of COLING. International Conference on Computational Linguistics","volume":"31 1","pages":"2734-2747"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75078118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1