A Gated Self-attention Memory Network for Answer Selection
Abstract
Answer selection is an important research problem, with applications in many areas. Previous deep learning based approaches for the task mainly adopt the Compare-Aggregate architecture that performs word-level comparison followed by aggregation. In this work, we take a departure from the popular Compare-Aggregate architecture, and instead, propose a new gated self-attention memory network for the task. Combined with a simple transfer learning technique from a large-scale online corpus, our model outperforms previous methods by a large margin, achieving new state-of-the-art results on two standard answer selection datasets: TrecQA and WikiQA.- Anthology ID:
- D19-1610
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5953–5959
- Language:
- URL:
- https://aclanthology.org/D19-1610
- DOI:
- 10.18653/v1/D19-1610
- Cite (ACL):
- Tuan Lai, Quan Hung Tran, Trung Bui, and Daisuke Kihara. 2019. A Gated Self-attention Memory Network for Answer Selection. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5953–5959, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- A Gated Self-attention Memory Network for Answer Selection (Lai et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D19-1610.pdf
- Code
- laituan245/StackExchangeQA
- Data
- WikiQA