Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network
This addresses the problem of complex reasoning across multiple documents for NLP researchers, with a significant but incremental improvement over existing methods.
The paper tackles multi-hop reading comprehension across documents by constructing a path-based reasoning graph and using a Gated-RGCN with question-aware gating, achieving state-of-the-art accuracy on WikiHop and surpassing human performance by 4.2% with an ensemble model.
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%.