Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. In this paper, we propose a new model to tackle the multi-hop RC problem. We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph. The advantage of HDE graph is that it contains different granularity levels of information including candidates, documents and entities in specific document contexts. Our proposed model can do reasoning over the HDE graph with nodes representation initialized with co-attention and self-attention based context encoders. We employ Graph Neural Networks (GNN) based message passing algorithms to accumulate evidences on the proposed HDE graph. Evaluated on the blind test set of the Qangaroo WikiHop data set, our HDE graph based model (single model) achieves state-of-the-art result.

本文提出了一种名为异构文档-实体图（HDE）的图形结构，并使用基于图神经网络的消息传递算法在HDE图上进行推理，以解决多跳阅读理解问题。实现了单个HDE模型的有竞争力的结果和集合模型的最先进性能.

通过异构图推理跨多个文档的多跳阅读理解