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Frontiers of Computer Science 2022年第5期16卷 85-96页

作者： Jiaqi LI Ming LIU Bing QIN Ting LIU School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Peng Cheng Laboratory Shenzhen 518052China

discourse parsing is an important research area in natural language processing(NLP),which aims to parse the discourse structure of coherent *** this survey,we introduce several different kinds of discourse parsing tasks,mainly including RST-style discourse parsing,PDTB-style discourse parsing,and discourse parsing for multiparty *** these tasks,we introduce the classical and recent existing methods,especially neural network *** that,we describe the applications of discourse parsing for other NLP tasks,such as machine reading comprehension and sentiment ***,we discuss the future trends of the task.

关键词： discourse parsing discourse structure RST PDTB STAC

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Representation learning in discourse parsing:A survey

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Science China(Technological Sciences) 2020年第10期63卷 1921-1946页

作者： SONG Wei LIU LiZhen College of Information Engineering and Academy for Multidisciplinary Studies Capital Normal UniversityBeijing 100056China

Neural network based deep learning methods aim to learn representations of data and have produced state-of-the-art results in many natural language processing(NLP)*** parsing is an important research topic in discourse analysis,aiming to infer the discourse structure and model the coherence of a given *** survey covers text-level discourse parsing,shallow discourse parsing and coherence *** first introduce the basic concepts and traditional approaches,and then focus on recent advances in discourse structure oriented representation *** also introduce a trend of discourse structure aware representation learning that is to exploit discourse structures or discourse objectives for learning representations of sentences and documents for specific applications or for general ***,we present a brief summary of the progress and discuss several future directions.

关键词： discourse analysis discourse parsing discourse relation coherence assessment representation learning

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Incorporating contextual evidence to improve implicit discourse relation recognition in Chinese

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Frontiers of Computer Science 2024年第3期18卷 91-104页

作者： Sheng XU Peifeng LI Qiaoming ZHU School of Computer Science and Technology Soochow UniversitySuzhou 215000China

The discourse analysis task,which focuses on understanding the semantics of long text spans,has received increasing attention in recent *** a critical component of discourse analysis,discourse relation recognition aims to identify the rhetorical relations between adjacent discourse units(e.g.,clauses,sentences,and sentence groups),called arguments,in a *** works focused on capturing the semantic interactions between arguments to recognize their discourse relations,ignoring important textual information in the surrounding ***,in many cases,more than capturing semantic interactions from the texts of the two arguments are needed to identify their rhetorical relations,requiring mining more contextual *** this paper,we propose a method to convert the RST-style discourse trees in the training set into dependency-based trees and train a contextual evidence selector on these transformed *** this way,the selector can learn the ability to automatically pick critical textual information from the context(i.e.,as evidence)for arguments to assist in discriminating their *** we encode the arguments concatenated with corresponding evidence to obtain the enhanced argument ***,we combine original and enhanced argument representations to recognize their *** addition,we introduce auxiliary tasks to guide the training of the evidence selector to strengthen its selection *** experimental results on the Chinese CDTB dataset show that our method outperforms several state-of-the-art baselines in both micro and macro F1 scores.

关键词： discourse parsing discourse relation recognition contextual evidence selection

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Top-down Text-Level discourse Rhetorical Structure parsing with Bidirectional Representation Learning

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Journal of Computer Science & Technology 2023年第5期38卷 985-1001页

作者：张龙印谭新孔芳李培峰周国栋 School of Computer Science and Technology Soochow UniversitySuzhou 215006China

Early studies on discourse rhetorical structure parsing mainly adopt bottom-up approaches,limiting the parsing process to local *** current top-down parsers can better capture global information and have achieved particular success,the importance of local and global information at various levels of discourse parsing is *** paper argues that combining local and global information for discourse parsing is more *** prove this,we introduce a top-down discourse parser with bidirectional representation learning *** corpora on Rhetorical Structure Theory(RST)are known to be much limited in size,which makes discourse parsing very *** alleviate this problem,we leverage some boundary features and a data augmentation strategy to tap the potential of our *** use two methods for evaluation,and the experiments on the RST-DT corpus show that our parser can pri-marily improve the performance due to the effective combination of local and global *** boundary features and the data augmentation strategy also play a *** on gold standard elementary discourse units(EDUs),our pars-er significantly advances the baseline systems in nuclearity detection,with the results on the other three indicators(span,relation,and full)being *** on automatically segmented EDUs,our parser still outperforms previous state-of-the-artwork.

关键词： discourse rhetorical structure discourse parsing representation learning

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