Machine reading comprehension (MRC) poses new challenges over logical reasoning, which aims to understand the implicit logical relations entailed in the given contexts and perform inference over them. Due to the complexity of logic, logical relations exist at different granularity levels. However, most existing methods of logical reasoning individually focus on either entity-aware or discourse-based information but ignore the hierarchical relations that may even have mutual effects. In this paper, we propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning, to provide a more fine-grained relation extraction. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism to improve the interpretation of MRC systems. Experimental results on logical reasoning QA datasets (ReClor and LogiQA) and natural language inference datasets (SNLI and ANLI) show the effectiveness and generalization of our method, and in-depth analysis verifies its capability to understand complex logical relations.

翻译：机器阅读理解（MRC）在逻辑推理领域提出了新挑战，旨在理解给定语境中隐含的逻辑关系并进行推理。由于逻辑的复杂性，逻辑关系存在于不同粒度层面。然而，现有大多数逻辑推理方法通常分别关注实体感知或话语层面的信息，却忽视了甚至可能互相影响的层次化关系。本文提出一种整体图网络（HGN），在话语层面和词汇层面处理上下文作为逻辑推理的基础，以实现更细粒度的关系抽取。具体而言，通过层次化交互机制建模节点级和类型级关系（可解释为推理过程中的桥梁），从而提升MRC系统的可解释性。在逻辑推理问答数据集（ReClor和LogiQA）及自然语言推理数据集（SNLI和ANLI）上的实验结果表明了所提方法的有效性和泛化能力，深入分析验证了其理解复杂逻辑关系的能力。