Implicit Discourse Relation Recognition (IDRR), which infers discourse relations without the help of explicit connectives, is still a crucial and challenging task for discourse parsing. Recent works tend to exploit the hierarchical structure information from the annotated senses, which demonstrate enhanced discourse relation representations can be obtained by integrating sense hierarchy. Nevertheless, the performance and robustness for IDRR are significantly constrained by the availability of annotated data. Fortunately, there is a wealth of unannotated utterances with explicit connectives, that can be utilized to acquire enriched discourse relation features. In light of such motivation, we propose a Prompt-based Logical Semantics Enhancement (PLSE) method for IDRR. Essentially, our method seamlessly injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction. Furthermore, considering the prompt-based connective prediction exhibits local dependencies due to the deficiency of masked language model (MLM) in capturing global semantics, we design a novel self-supervised learning objective based on mutual information maximization to derive enhanced representations of logical semantics for IDRR. Experimental results on PDTB 2.0 and CoNLL16 datasets demonstrate that our method achieves outstanding and consistent performance against the current state-of-the-art models.

翻译：隐式篇章关系识别（IDRR）旨在无需显式连接词的情况下推断篇章关系，这仍是篇章解析中一项关键且具有挑战性的任务。近期研究倾向于利用标注语义中的层次结构信息，表明通过整合语义层次可获得增强的篇章关系表示。然而，IDRR的性能和鲁棒性受到标注数据可用性的显著制约。幸运的是，存在大量带有显式连接词的未标注话语，可用于获取丰富的篇章关系特征。基于这一动机，我们提出了一种基于提示的逻辑语义增强（PLSE）方法用于IDRR。本质上，我们的方法通过基于提示的连接词预测，将与篇章关系相关的知识无缝注入预训练语言模型。此外，考虑到基于提示的连接词预测因掩码语言模型（MLM）在捕获全局语义方面的不足而表现出局部依赖性，我们设计了一种基于互信息最大化的新型自监督学习目标，以推导用于IDRR的增强逻辑语义表示。在PDTB 2.0和CoNLL16数据集上的实验结果表明，我们的方法相比当前最先进模型取得了出色且一致的性能。