A Review on Deep Learning Techniques Applied to Answer Selection

Tuan Manh Lai, Trung Bui, Sheng Li


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.
Anthology ID:
C18-1181
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2132–2144
Language:
URL:
https://aclanthology.org/C18-1181
DOI:
Bibkey:
Cite (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.
Cite (Informal):
A Review on Deep Learning Techniques Applied to Answer Selection (Lai et al., COLING 2018)
Copy Citation:
PDF:
https://aclanthology.org/C18-1181.pdf
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