Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?

Laurine Huber, Chaker Memmadi, Mathilde Dargnat, Y. Toussaint
{"title":"Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?","authors":"Laurine Huber, Chaker Memmadi, Mathilde Dargnat, Y. Toussaint","doi":"10.18653/v1/2020.codi-1.9","DOIUrl":null,"url":null,"abstract":"We introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence and cohesion between clauses of a sentence, and discourse relations within sentences. We show that even if contextual encoders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well.","PeriodicalId":332037,"journal":{"name":"Proceedings of the First Workshop on Computational Approaches to Discourse","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the First Workshop on Computational Approaches to Discourse","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2020.codi-1.9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

We introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence and cohesion between clauses of a sentence, and discourse relations within sentences. We show that even if contextual encoders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
句子嵌入是否捕获了科学摘要中句子的话语属性?
我们介绍了四个任务,旨在确定哪些句子编码器最能捕获科学摘要句子的话语属性,即句子的子句之间的连贯性和凝聚力,以及句子内的话语关系。研究表明,即使BERT或SciBERT等语境编码器对语篇单位的连贯性进行编码,它们也无法预测科学摘要中常用的三种语篇关系。我们讨论了这些结果所强调的内容,即这些话语关系基于特定的措辞,这些措辞允许非上下文编码器表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ? Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing Joint Modeling of Arguments for Event Understanding Coreference for Discourse Parsing: A Neural Approach Computational Interpretation of Recency for the Choice of Referring Expressions in Discourse
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1