@inproceedings{yuan-etal-2020-enhancing,
title = "Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension",
author = "Yuan, Fei and
Shou, Linjun and
Bai, Xuanyu and
Gong, Ming and
Liang, Yaobo and
Duan, Nan and
Fu, Yan and
Jiang, Daxin",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.87",
doi = "10.18653/v1/2020.acl-main.87",
pages = "925--934",
abstract = "Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages. However, the transfer quality for multilingual Machine Reading Comprehension (MRC) is significantly worse than sentence classification tasks mainly due to the requirement of MRC to detect the word level answer boundary. In this paper, we propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision: (1) A mixed MRC task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs; (2) A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web. Besides, extensive experiments on two cross-lingual MRC datasets show the effectiveness of our proposed approach.",
}
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<abstract>Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages. However, the transfer quality for multilingual Machine Reading Comprehension (MRC) is significantly worse than sentence classification tasks mainly due to the requirement of MRC to detect the word level answer boundary. In this paper, we propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision: (1) A mixed MRC task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs; (2) A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web. Besides, extensive experiments on two cross-lingual MRC datasets show the effectiveness of our proposed approach.</abstract>
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%0 Conference Proceedings
%T Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension
%A Yuan, Fei
%A Shou, Linjun
%A Bai, Xuanyu
%A Gong, Ming
%A Liang, Yaobo
%A Duan, Nan
%A Fu, Yan
%A Jiang, Daxin
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F yuan-etal-2020-enhancing
%X Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages. However, the transfer quality for multilingual Machine Reading Comprehension (MRC) is significantly worse than sentence classification tasks mainly due to the requirement of MRC to detect the word level answer boundary. In this paper, we propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision: (1) A mixed MRC task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs; (2) A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web. Besides, extensive experiments on two cross-lingual MRC datasets show the effectiveness of our proposed approach.
%R 10.18653/v1/2020.acl-main.87
%U https://aclanthology.org/2020.acl-main.87
%U https://doi.org/10.18653/v1/2020.acl-main.87
%P 925-934
Markdown (Informal)
[Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension](https://aclanthology.org/2020.acl-main.87) (Yuan et al., ACL 2020)
ACL