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.
@article{arxiv.1809.06963,
title = {Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension},
author = {Yichong Xu and Xiaodong Liu and Yelong Shen and Jingjing Liu and Jianfeng Gao},
journal= {arXiv preprint arXiv:1809.06963},
year = {2019}
}
Comments
North American Chapter of the Association for Computational Linguistics (NAACL) 2019