Adaptive Prompt Learning with Distilled Connective Knowledge for
  Implicit Discourse Relation Recognition
Bang Wang Zhenglin Wang Wei Xiang Yijun Mo
Implicit discourse relation recognition (IDRR) aims at recognizing the
discourse relation between two text segments without an explicit connective.
Recently, the prompt learning has just been applied to the IDRR task with great
performance improvements over various neural network-based approaches. However,
the discrete nature of the state-art-of-art prompting approach requires manual
design of templates and answers, a big hurdle for its practical applications.
In this paper, we propose a continuous version of prompt learning together with
connective knowledge distillation, called AdaptPrompt, to reduce manual design
efforts via continuous prompting while further improving performance via
knowledge transfer. In particular, we design and train a few virtual tokens to
form continuous templates and automatically select the most suitable one by
gradient search in the embedding space. We also design an answer-relation
mapping rule to generate a few virtual answers as the answer space.
Furthermore, we notice the importance of annotated connectives in the training
dataset and design a teacher-student architecture for knowledge transfer.
Experiments on the up-to-date PDTB Corpus V3.0 validate our design objectives
in terms of the better relation recognition performance over the
state-of-the-art competitors.