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

• URL: http://arxiv.org/abs/2006.06478v2
• Date: Fri, 12 Jun 2020 04:04:05 GMT
• Title: Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network
• Authors: Zeyun Tang, Yongliang Shen, Xinyin Ma, Wei Xu, Jiale Yu, Weiming Lu
• Abstract summary: We propose a novel approach to tackle this multi-hop reading comprehension problem. Inspired by human reasoning processing, we construct a path-based reasoning graph from supporting documents. We evaluate our approach on WikiHop dataset, and our approach achieves state-of-the-art accuracy against previously published approaches.
• Score: 20.180529733311165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-hop reading comprehension across multiple documents attracts much attention recently. In this paper, we propose a novel approach to tackle this multi-hop reading comprehension problem. Inspired by human reasoning processing, we construct a path-based reasoning graph from supporting documents. This graph can combine both the idea of the graph-based and path-based approaches, so it is better for multi-hop reasoning. Meanwhile, we propose Gated-RGCN to accumulate evidence on the path-based reasoning graph, which contains a new question-aware gating mechanism to regulate the usefulness of information propagating across documents and add question information during reasoning. We evaluate our approach on WikiHop dataset, and our approach achieves state-of-the-art accuracy against previously published approaches. Especially, our ensemble model surpasses human performance by 4.2%.

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