One vs. Many QA Matching with both Word-level and Sentence-level Attention Network
Lu Wang, Shoushan Li, Changlong Sun, Luo Si, Xiaozhong Liu, Min Zhang, Guodong Zhou
Abstract
Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community. In this paper, we first build a novel QA matching corpus with informal text which is collected from a product reviewing website. Then, we propose a novel QA matching approach, namely One vs. Many Matching, which aims to address the novel scenario where one question sentence often has an answer with multiple sentences. Furthermore, we improve our matching approach by employing both word-level and sentence-level attentions for solving the noisy problem in the informal text. Empirical studies demonstrate the effectiveness of the proposed approach to question-answer matching.- Anthology ID:
- C18-1215
- 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:
- 2540–2550
- Language:
- URL:
- https://aclanthology.org/C18-1215
- DOI:
- Cite (ACL):
- Lu Wang, Shoushan Li, Changlong Sun, Luo Si, Xiaozhong Liu, Min Zhang, and Guodong Zhou. 2018. One vs. Many QA Matching with both Word-level and Sentence-level Attention Network. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2540–2550, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (Wang et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/C18-1215.pdf
- Data
- WikiQA