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        <title>A Review on Deep Learning Techniques Applied to Answer Selection</title>
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    <abstract>Given a question and a set of candidate answers, answer selection is the task of identifying which of the candidates answers the question correctly. It is an important problem in natural language processing, with applications in many areas. Recently, many deep learning based methods have been proposed for the task. They produce impressive performance without relying on any feature engineering or expensive external resources. In this paper, we aim to provide a comprehensive review on deep learning methods applied to answer selection.</abstract>
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%0 Conference Proceedings
%T A Review on Deep Learning Techniques Applied to Answer Selection
%A Lai, Tuan Manh
%A Bui, Trung
%A Li, Sheng
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F lai-etal-2018-review
%X Given a question and a set of candidate answers, answer selection is the task of identifying which of the candidates answers the question correctly. It is an important problem in natural language processing, with applications in many areas. Recently, many deep learning based methods have been proposed for the task. They produce impressive performance without relying on any feature engineering or expensive external resources. In this paper, we aim to provide a comprehensive review on deep learning methods applied to answer selection.
%U https://aclanthology.org/C18-1181
%P 2132-2144

Markdown (Informal)

[A Review on Deep Learning Techniques Applied to Answer Selection](https://aclanthology.org/C18-1181) (Lai et al., COLING 2018)

• A Review on Deep Learning Techniques Applied to Answer Selection (Lai et al., COLING 2018)

ACL

• Tuan Manh Lai, Trung Bui, and Sheng Li. 2018. A Review on Deep Learning Techniques Applied to Answer Selection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2132–2144, Santa Fe, New Mexico, USA. Association for Computational Linguistics.