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Due to the absence of connectives, implicit discourse relation recognition(IDRR) is still a challenging and crucial task in discourse analysis. Most ofthe current work adopted multi-task learning to aid IDRR through explicitdiscourse relation recognition (EDRR) or utilized dependencies betweendiscourse relation labels to constrain model predictions. But these methodsstill performed poorly on fine-grained IDRR and even utterly misidentified onmost of the few-shot discourse relation classes. To address these problems, wepropose a novel Prompt-based Connective Prediction (PCP) method for IDRR. Ourmethod instructs large-scale pre-trained models to use knowledge relevant todiscourse relation and utilizes the strong correlation between connectives anddiscourse relation to help the model recognize implicit discourse relations.Experimental results show that our method surpasses the currentstate-of-the-art model and achieves significant improvements on thosefine-grained few-shot discourse relation. Moreover, our approach is able to betransferred to EDRR and obtain acceptable results. Our code is released inhttps://github.com/zh-i9/PCP-for-IDRR.

Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition