Due to the absence of explicit connectives, implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis. The critical step for IDRR is to learn high-quality discourse relation representations between two arguments. Recent methods tend to integrate the whole hierarchical information of senses into discourse relation representations for multi-level sense recognition. Nevertheless, they insufficiently incorporate the static hierarchical structure containing all senses (defined as global hierarchy), and ignore the hierarchical sense label sequence corresponding to each instance (defined as local hierarchy). For the purpose of sufficiently exploiting global and local hierarchies of senses to learn better discourse relation representations, we propose a novel GlObal and Local Hierarchy-aware Contrastive Framework (GOLF), to model two kinds of hierarchies with the aid of multi-task learning and contrastive learning. Experimental results on PDTB 2.0 and PDTB 3.0 datasets demonstrate that our method remarkably outperforms current state-of-the-art models at all hierarchical levels. Our code is publicly available at https://github.com/YJiangcm/GOLF_for_IDRR

翻译：由于缺乏显式连接词，隐式话语关系识别（Implicit Discourse Relation Recognition，IDRR）仍是话语分析中的一项挑战性任务。IDRR的关键步骤在于学习两个论元之间的高质量话语关系表示。现有方法倾向于将语义标签的完整层级信息整合到话语关系表示中，以实现多层级语义识别。然而，这些方法未能充分纳入包含所有语义标签的静态层级结构（定义为全局层级），且忽略了与每个实例对应的层级化语义标签序列（定义为局部层级）。为充分利用语义标签的全局与局部层级信息，以学习更优的话语关系表示，我们提出了一种新颖的全局与局部层级感知对比框架（GLObal and Local Hierarchy-aware Contrastive Framework，GOLF），借助多任务学习与对比学习对这两种层级结构进行建模。在PDTB 2.0和PDTB 3.0数据集上的实验结果表明，我们的方法在所有层级上均显著优于当前最先进模型。我们的代码已公开于https://github.com/YJiangcm/GOLF_for_IDRR。