Implicit discourse relation recognition with multi-view contrastive learning
Previous researches on implicit discourse relationship recognition(IDRR)usually focus on designing effective discourse encoders.Different from theirs,this paper proposes a novel approach which introduces contrastive learning into IDRR so as to obtain representations of discourse units(DUs)with more differentiation.Specifically,a lightweight IDRR classification model is firstly adopted.Then,to better learn representations of DUs,the application of three different contrastive learning methods in IDDR are explored from multiple views,including instance-level,batch-level,and group-level.Finally,three multi-view contrastive learning objectives are combined for better IDRR.Our pro-posed method only slightly increases training time and introduces small additional parameters.Experi-mental results on PDTB 2.0 show that our method achieves the state-of-the-art performance.
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