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.

翻译：隐式话语关系识别（IDRR）旨在识别两个文本片段之间不含显式连接词的话语关系。近年来，提示学习刚被应用于IDRR任务，并在多种基于神经网络的方法上取得了显著性能提升。然而，当前最先进的提示方法因其离散特性需要手动设计模板和答案，这成为其实践应用的主要障碍。本文提出一种融合连接性知识蒸馏的连续版本提示学习方法，称为AdaptPrompt，通过连续提示减少人工设计工作量，并借助知识迁移进一步提升性能。具体而言，我们设计并训练若干虚拟标记形成连续模板，通过嵌入空间的梯度搜索自动选择最优模板；同时设计答案-关系映射规则生成虚拟答案空间。此外，我们还注意到训练数据集中标注连接词的重要性，并设计了一种师生架构用于知识迁移。在最新PDTB语料库V3.0上的实验验证了我们的设计目标：相比最先进的竞争方法，本方法在关系识别性能上表现更优。