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Multi-hop reasoning question answering requires deep comprehension ofrelationships between various documents and queries. We propose aBi-directional Attention Entity Graph Convolutional Network (BAG), leveragingrelationships between nodes in an entity graph and attention informationbetween a query and the entity graph, to solve this task. Graph convolutionalnetworks are used to obtain a relation-aware representation of nodes for entitygraphs built from documents with multi-level features. Bidirectional attentionis then applied on graphs and queries to generate a query-aware nodesrepresentation, which will be used for the final prediction. Experimentalevaluation shows BAG achieves state-of-the-art accuracy performance on theQAngaroo WIKIHOP dataset.

BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering