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.
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2022, Vol. 36 
