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.

翻译：多动推理问题解答要求深入理解各种文件和查询之间的关系。我们提议建立一个双向注意实体图表网络(BAG),利用实体图中的节点和查询与实体图之间的注意信息之间的关系来解决这个问题。图集网络用于获取具有多层次特征的文件所建实体图的相关节点表示。然后,对图表和查询进行双向注意,以生成查询觉结节点代表,用于最终预测。实验性评估显示,BAG在QAngaroo WIKIHOP数据集上取得了最先进的精确性能。