TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse
  Relation Recognition
Wei Xiang Chao Liang Bang Wang
Implicit Discourse Relation Recognition (IDRR) aims at classifying the
relation sense between two arguments without an explicit connective. Recently,
the ConnPrompt~\cite{Wei.X:et.al:2022:COLING} has leveraged the powerful prompt
learning for IDRR based on the fusion of multi-prompt decisions from three
different yet much similar connective prediction templates. Instead of
multi-prompt ensembling, we propose to design auxiliary tasks with enlightened
prompt learning for the IDRR task. Although an auxiliary task is not used to
directly output final prediction, we argue that during the joint training some
of its learned features can be useful to boost the main task. In light of such
motivations, we propose a task enlightenment prompt learning model, called
TEPrompt, to fuse learned features from three related tasks for IDRR. In
particular, the TEPrompt contains three tasks, viz., Discourse Relation
Recognition (DRR), Sense Semantics Classification (SSC) and Annotated
Connective Prediction (ACP), each with a unique prompt template and an answer
space. In the training phase, we jointly train three prompt learning tasks with
shared argument representation. In the testing phase, we only take the DRR
output with fused features as the final IDRR decision. Experiments with the
same conditions have shown that the proposed TEPrompt outperforms the
ConnPrompt. This can be attributed to the promoted decision features and
language models benefited from joint-training of auxiliary tasks.