A Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension

Multi-hop machine reading comprehension is a challenging task in natural language processing, which requires more reasoning ability across multiple documents. Spectral models based on graph convolutional networks grant inferring abilities and lead to competitive results. However, part of them still faces the challenge of analyzing the reasoning in a human-understandable way. Inspired by the concept of the Grandmother Cells in cognitive neuroscience, a spatial graph attention framework named ClueReader was proposed in this paper, imitating the procedure. This model is designed to assemble the semantic features in multi-level representations and automatically concentrate or alleviate information for reasoning via the attention mechanism. The name ClueReader is a metaphor for the pattern of the model: regard the subjects of queries as the start points of clues, take the reasoning entities as bridge points, consider the latent candidate entities as the grandmother cells, and the clues end up in candidate entities. The proposed model allows us to visualize the reasoning graph, then analyze the importance of edges connecting two entities and the selectivity in the mention and candidate nodes, which can be easier to be comprehended empirically. The official evaluations in the open-domain multi-hop reading dataset WikiHop and the Drug-drug Interactions dataset MedHop prove the validity of our approach and show the probability of the application of the model in the molecular biology domain.