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
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2132–2144
- Language:
- URL:
- https://aclanthology.org/C18-1181
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/C18-1181.pdf
- Data
- InsuranceQA, TrecQA, WikiQA