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