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.

翻译：隐式话语关系识别（IDRR）旨在对两个论元之间无显式连接词的关系语义进行分类。近期，ConnPrompt~\cite{Wei.X:et.al:2022:COLING}基于三种不同但高度相似的连接词预测模板的多提示决策融合，利用强大的提示学习方法实现了IDRR。不同于多提示集成，我们提出通过启迪式提示学习为IDRR任务设计辅助任务。尽管辅助任务不直接输出最终预测，但我们在联合训练过程中发现，其部分学习到的特征有助于提升主任务性能。基于此动机，我们提出一种任务启迪提示学习模型TEPrompt，用于融合三个相关任务的学习特征以实现IDRR。具体而言，TEPrompt包含三个任务：话语关系识别（DRR）、语义类别分类（SSC）和标注连接词预测（ACP），每个任务均具有独特的提示模板和答案空间。在训练阶段，我们以共享论元表征联合训练三个提示学习任务；在测试阶段，仅采用融合特征的DRR输出作为最终IDRR决策。相同实验条件下的结果表明，所提TEPrompt优于ConnPrompt。这归因于辅助任务联合训练所增强的决策特征与语言模型。