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Multi-hop reading comprehension across multiple documents attracts muchattention recently. In this paper, we propose a novel approach to tackle thismulti-hop reading comprehension problem. Inspired by human reasoningprocessing, we construct a path-based reasoning graph from supportingdocuments. This graph can combine both the idea of the graph-based andpath-based approaches, so it is better for multi-hop reasoning. Meanwhile, wepropose Gated-RGCN to accumulate evidence on the path-based reasoning graph,which contains a new question-aware gating mechanism to regulate the usefulnessof information propagating across documents and add question information duringreasoning. We evaluate our approach on WikiHop dataset, and our approachachieves state-of-the-art accuracy against previously published approaches.Especially, our ensemble model surpasses human performance by 4.2%.

Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network