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        <title>BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering
%A Cao, Yu
%A Fang, Meng
%A Tao, Dacheng
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F cao-etal-2019-bag
%X 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.
%R 10.18653/v1/N19-1032
%U https://aclanthology.org/N19-1032
%U https://doi.org/10.18653/v1/N19-1032
%P 357-362

Markdown (Informal)

[BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering](https://aclanthology.org/N19-1032) (Cao et al., NAACL 2019)

• BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering (Cao et al., NAACL 2019)

ACL

• Yu Cao, Meng Fang, and Dacheng Tao. 2019. BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 357–362, Minneapolis, Minnesota. Association for Computational Linguistics.