A Systematic Investigation of Neural Models for Chinese Implicit Discourse Relationship Recognition

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 2019 International Conference on Asian Language Processing, IALP 2019
EditorsMan Lan, Yuanbin Wu, Minghui Dong, Yanfeng Lu, Yan Yang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages390-395
Number of pages6
ISBN (Electronic)9781728150147
DOIs
StatePublished - Nov 2019
Event23rd International Conference on Asian Language Processing, IALP 2019 - Shanghai, China
Duration: 15 Nov 201917 Nov 2019

Publication series

NameProceedings of the 2019 International Conference on Asian Language Processing, IALP 2019

Conference

Conference23rd International Conference on Asian Language Processing, IALP 2019
Country/TerritoryChina
CityShanghai
Period15/11/1917/11/19

Keywords

  • Chinese implicit discourse relation recognition
  • deep learning
  • word embedding

Fingerprint

Dive into the research topics of 'A Systematic Investigation of Neural Models for Chinese Implicit Discourse Relationship Recognition'. Together they form a unique fingerprint.

Cite this