Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension

Yichong Xu, Xiaodong Liu, Yelong Shen, Jingjing Liu, Jianfeng Gao


Abstract
We propose a multi-task learning framework to learn a joint Machine Reading Comprehension (MRC) model that can be applied to a wide range of MRC tasks in different domains. Inspired by recent ideas of data selection in machine translation, we develop a novel sample re-weighting scheme to assign sample-specific weights to the loss. Empirical study shows that our approach can be applied to many existing MRC models. Combined with contextual representations from pre-trained language models (such as ELMo), we achieve new state-of-the-art results on a set of MRC benchmark datasets. We release our code at https://github.com/xycforgithub/MultiTask-MRC.
Anthology ID:
N19-1271
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2644–2655
Language:
URL:
https://aclanthology.org/N19-1271
DOI:
10.18653/v1/N19-1271
Bibkey:
Cite (ACL):
Yichong Xu, Xiaodong Liu, Yelong Shen, Jingjing Liu, and Jianfeng Gao. 2019. Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2644–2655, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension (Xu et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1271.pdf
Video:
 https://vimeo.com/364750438
Code
 xycforgithub/MultiTask-MRC +  additional community code
Data
MS MARCONewsQASQuAD