  Multi-hop reasoning question answering requires deep comprehension of
relationships between various documents and queries. We propose a
Bi-directional Attention Entity Graph Convolutional Network (BAG), leveraging
relationships between nodes in an entity graph and attention information
between a query and the entity graph, to solve this task. Graph convolutional
networks are used to obtain a relation-aware representation of nodes for entity
graphs built from documents with multi-level features. Bidirectional attention
is then applied on graphs and queries to generate a query-aware nodes
representation, which will be used for the final prediction. Experimental
evaluation shows BAG achieves state-of-the-art accuracy performance on the
QAngaroo WIKIHOP dataset.
