Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension

Zhen Wang, Jiachen Liu, Xinyan Xiao, Yajuan Lyu, Tian Wu


Abstract
While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates. This problem can be even worse in open-domain scenarios, where candidates from multiple passages should be combined to answer a single question. In this paper, we formulate reading comprehension as an extract-then-select two-stage procedure. We first extract answer candidates from passages, then select the final answer by combining information from all the candidates. Furthermore, we regard candidate extraction as a latent variable and train the two-stage process jointly with reinforcement learning. As a result, our approach has improved the state-of-the-art performance significantly on two challenging open-domain reading comprehension datasets. Further analysis demonstrates the effectiveness of our model components, especially the information fusion of all the candidates and the joint training of the extract-then-select procedure.
Anthology ID:
P18-1159
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1715–1724
Language:
URL:
https://aclanthology.org/P18-1159
DOI:
10.18653/v1/P18-1159
Bibkey:
Cite (ACL):
Zhen Wang, Jiachen Liu, Xinyan Xiao, Yajuan Lyu, and Tian Wu. 2018. Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1715–1724, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension (Wang et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1159.pdf
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