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
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2540–2550
Language:
URL:
https://aclanthology.org/C18-1215
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/C18-1215.pdf
Data
WikiQA