  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.
