DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse
  Relation Recognition
Chunkit Chan Xin Liu Jiayang Cheng Zihan Li Yangqiu Song Ginny Y. Wong Simon See
Implicit Discourse Relation Recognition (IDRR) is a sophisticated and
challenging task to recognize the discourse relations between the arguments
with the absence of discourse connectives. The sense labels for each discourse
relation follow a hierarchical classification scheme in the annotation process
(Prasad et al., 2008), forming a hierarchy structure. Most existing works do
not well incorporate the hierarchy structure but focus on the syntax features
and the prior knowledge of connectives in the manner of pure text
classification. We argue that it is more effective to predict the paths inside
the hierarchical tree (e.g., "Comparison -> Contrast -> however") rather than
flat labels (e.g., Contrast) or connectives (e.g., however). We propose a
prompt-based path prediction method to utilize the interactive information and
intrinsic senses among the hierarchy in IDRR. This is the first work that
injects such structure information into pre-trained language models via prompt
tuning, and the performance of our solution shows significant and consistent
improvement against competitive baselines.