论文标题
全球和本地层次结构意识的对比框架，用于隐式话语关系识别
Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition
论文作者
论文摘要
由于缺乏明确的连接剂，隐式话语关系识别（IDRR）仍然是话语分析中的一项挑战。 IDRR的关键步骤是学习两个参数之间的高质量话语关系表示。最近的方法倾向于将感官的整个层次信息整合到多层意识识别的话语关系表示中。然而，它们不足以结合包含所有感官（定义为全局层次结构）的静态层次结构，并忽略了与每个实例相对应的层次结构sense标签序列（定义为局部层次结构）。为了充分利用全球和本地感官的层次结构以学习更好的话语关系表示，我们提出了一种新颖的全球和本地层次结构 - 意识到的对比框架（高尔夫），以借助多任务的学习和对比学习和对比学习来建模两种层次结构。 PDTB 2.0和PDTB 3.0数据集的实验结果表明，我们的方法在所有层次级别上都明显优于当前最新模型。我们的代码可在https://github.com/yjiangcm/golf_for_idrr上公开获取
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
