基于高困惑样本对比学习的隐式篇章关系识别

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Abstract Implicit discourse relation recognition automatically identifies the semantic relation between arguments. The key to this task involves two issues: one is to represent the argument semantics, the other is to recognize the relation between arguments. Focusing on better representation of the arguments, this paper introduces the contrast learning into the process of argument representation learning. We further propose a method generating confused samples based on conditional auto-encoders, so as to enhance the confused data in contrastive learning. Experiments on the Penn Discourse Treebank (PDTB) corpus show that,our method increases F₁ score by 4.68%, 4.63%, 3.14% and 12.77% on four top relations (Comparison, Contingency, Expansion, and Temporal), respectively.

Key words： implicit discourse relation recognition contrastive learning condition variational auto-encoder

Received: 13 January 2022

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	Articles by authors
	LI Xiao
	HONG Yu
	DOU Zujun
	XU Minhan
	LU Yuxiang
	ZHOU Guodong

Cite this article:

LI Xiao,HONG Yu,DOU Zujun, et al. Contrastive Learning with Confused Samples for Implicit Discourse Relation Recognition[J]. , 2022, 36(11): 38-49.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I11/38

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