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
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/N19-1032.pdf
- Code
- caoyu1991/BAG
- Data
- SQuAD, WikiHop