@inproceedings{lai-etal-2019-gated,
title = "A Gated Self-attention Memory Network for Answer Selection",
author = "Lai, Tuan and
Tran, Quan Hung and
Bui, Trung and
Kihara, Daisuke",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1610/",
doi = "10.18653/v1/D19-1610",
pages = "5953--5959",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T A Gated Self-attention Memory Network for Answer Selection
%A Lai, Tuan
%A Tran, Quan Hung
%A Bui, Trung
%A Kihara, Daisuke
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F lai-etal-2019-gated
%X 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.
%R 10.18653/v1/D19-1610
%U https://aclanthology.org/D19-1610/
%U https://doi.org/10.18653/v1/D19-1610
%P 5953-5959
Markdown (Informal)
[A Gated Self-attention Memory Network for Answer Selection](https://aclanthology.org/D19-1610/) (Lai et al., EMNLP-IJCNLP 2019)
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.