{"title":"A Systematic Investigation of Neural Models for Chinese Implicit Discourse Relationship Recognition","authors":"Dejian Li, Man Lan, Yuanbin Wu","doi":"10.1109/IALP48816.2019.9037686","DOIUrl":null,"url":null,"abstract":"The Chinese implicit discourse relationship recognition is more challenging than English due to the lack of discourse connectives and high frequency in the text. So far, there is no systematical investigation into the neural components for Chinese implicit discourse relationship. To fill this gap, in this work we present a component-based neural framework to systematically study the Chinese implicit discourse relationship. Experimental results showed that our proposed neural Chinese implicit discourse parser achieves the SOTA performance in CoNLL-2016 corpus.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Asian Language Processing (IALP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP48816.2019.9037686","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
The Chinese implicit discourse relationship recognition is more challenging than English due to the lack of discourse connectives and high frequency in the text. So far, there is no systematical investigation into the neural components for Chinese implicit discourse relationship. To fill this gap, in this work we present a component-based neural framework to systematically study the Chinese implicit discourse relationship. Experimental results showed that our proposed neural Chinese implicit discourse parser achieves the SOTA performance in CoNLL-2016 corpus.
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