首页|Incorporating contextual evidence to improve implicit discourse relation recognition in Chinese

Incorporating contextual evidence to improve implicit discourse relation recognition in Chinese

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原文链接
  • NETL
  • NSTL
  • 万方数据
The discourse analysis task,which focuses on understanding the semantics of long text spans,has received increasing attention in recent years.As 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 document.Previous works focused on capturing the semantic interactions between arguments to recognize their discourse relations,ignoring important textual information in the surrounding contexts.However,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 clues.In 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 structures.In 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 relations.Then we encode the arguments concatenated with corresponding evidence to obtain the enhanced argument representations.Finally,we combine original and enhanced argument representations to recognize their relations.In addition,we introduce auxiliary tasks to guide the training of the evidence selector to strengthen its selection ability.The 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.

discourse parsingdiscourse relation recognitioncontextual evidence selection

Sheng XU、Peifeng LI、Qiaoming ZHU

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School of Computer Science and Technology,Soochow University,Suzhou 215000,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaPriority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions

6183600761773276

2024

10.1007/s11704-023-2503-4

计算机科学前沿
高等教育出版社

计算机科学前沿

CSTPCDEI
影响因子：0.303
ISSN：2095-2228
年,卷(期)：2024.18(3)
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