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

Understand

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.