Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation

Implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis due to the absence of connectives. Most existing methods utilize one-hot labels as the sole optimization target, ignoring the internal association among connectives. Besides, these approaches spend lots of effort on template construction, negatively affecting the generalization capability. To address these problems, we propose a novel Connective Prediction via Knowledge Distillation (CP-KD) approach to instruct large-scale pre-trained language models (PLMs) mining the latent correlations between connectives and discourse relations, which is meaningful for IDRR. Experimental results on the PDTB 2.0/3.0 and CoNLL 2016 datasets show that our method significantly outperforms the state-of-the-art models on coarse-grained and fine-grained discourse relations. Moreover, our approach can be transferred to explicit discourse relation recognition (EDRR) and achieve acceptable performance. Our code is released in https://github . com/cubenlp/CP_KD-for-IDRR.