@inproceedings{xu-etal-2019-multi,
title = "Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension",
author = "Xu, Yichong and
Liu, Xiaodong and
Shen, Yelong and
Liu, Jingjing and
Gao, Jianfeng",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1271",
doi = "10.18653/v1/N19-1271",
pages = "2644--2655",
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 \url{https://github.com/xycforgithub/MultiTask-MRC}.",
}
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%0 Conference Proceedings
%T Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
%A Xu, Yichong
%A Liu, Xiaodong
%A Shen, Yelong
%A Liu, Jingjing
%A Gao, Jianfeng
%S 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)
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F xu-etal-2019-multi
%X 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.
%R 10.18653/v1/N19-1271
%U https://aclanthology.org/N19-1271
%U https://doi.org/10.18653/v1/N19-1271
%P 2644-2655
Markdown (Informal)
[Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension](https://aclanthology.org/N19-1271) (Xu et al., NAACL 2019)
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.