Prompt-based Logical Semantics Enhancement for Implicit Discourse
  Relation Recognition
Chenxu Wang Ping Jian Mu Huang
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.