NCP rompt: NSP -Based Prompt Learning and Contrastive Learning for Implicit Discourse Relation Recognition (2024)

Abstract: Implicit Discourse Relation Recognition (IDRR) is an important task to classify the discourse relation sense between argument pairs without an explicit connective. Recently, prompt learning methods have demonstrated success in IDRR. However, prior work primarily transform IDRR into a connective-cloze task based on the masked language model (MLM), which limits the predicted connective to one single token. Also, they fail to fully exploit critical semantic features shared among various forms of templates. In this paper, we propose NCPrompt, an NSP-based prompt learning and Contrastive learning method for IDRR. Specifically, we transform the IDRR task into a next sentence prediction (NSP) task, which can allow various-length answer connectives and enlarge the construction of the verbalizer for prompt-learning methods. Also, we notice that various prompt templates naturally constitute positive samples applied for self-supervised contrastive learning. And the usage of NSP naturally creates hard negative samples by introducing different candidate connectives between the same example. To our knowledge, we are the first to combine self-supervised contrastive learning with prompt learning to obtain high-quality semantic representations. Experiments on the PDTB 3.0 corpus have demonstrated the effectiveness and superiority of our model.

## introduction implicit discourse relation recognition (idrr) aims at classifying the relation sense between a pair of text segments (called arguments) without an explicit connective @xcite . idrr provides essential information for many natural language processing (nlp) tasks, such as question answering @xcite ) and machine this work is supported by hubei intelligent edge computing research institute, hubei science and technology talent service project (2024djc078). ## the proposed ncprompt model figure 2 presents the overview of our ncprompt model. overall, we first design prompts to reformulate the idrr task into an nsp task. the plm outputs the coherence score of the argument pair connected by each connective from the answer space. during training, we design positives and negatives for self-supervised contrastive learning, and combine the contrastive loss with the connective classification loss. during testing, the connective with the highest coherence score is mapped into the answer relation sense label through the verbalizer. ## conclusion in this paper, we first apply the nsp-based prompt learning method for idrr and combine the selfsupervised contrastive learning. experiments on the pdtb 3.0 corpus validate the effectiveness of our proposed ncprompt. we offer a new perspective that nsp-based prompt learning methods can be as remarkable as the commonly-used mlmbased ones, and validate the applicability of selfsupervised contrastive learning in nlp.