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

Yu Cao, Meng Fang, Dacheng Tao


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.
Anthology ID:
N19-1032
Volume:
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)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
357–362
Language:
URL:
https://aclanthology.org/N19-1032
DOI:
10.18653/v1/N19-1032
Bibkey:
Cite (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.
Cite (Informal):
BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering (Cao et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1032.pdf
Code
 caoyu1991/BAG
Data
SQuADWikiHop