@inproceedings{wu-etal-2019-improving,
title = "Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior",
author = "Wu, Bowen and
Huang, Haoyang and
Wang, Zongsheng and
Feng, Qihang and
Yu, Jingsong and
Wang, Baoxun",
editor = "Fisch, Adam and
Talmor, Alon and
Jia, Robin and
Seo, Minjoon and
Choi, Eunsol and
Chen, Danqi",
booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5807",
doi = "10.18653/v1/D19-5807",
pages = "53--57",
abstract = "Despite the remarkable progress on Machine Reading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfortunately suffer from weak robustness against adversarial samples. To address this issue, we attempt to take sentence syntax as the leverage in the answer predicting process which previously only takes account of phrase-level semantics. Furthermore, to better utilize the sentence syntax and improve the robustness, we propose a Syntactic Leveraging Network, which is designed to deal with adversarial samples by exploiting the syntactic elements of a question. The experiment results indicate that our method is promising for improving the generalization and robustness of MRC models against the influence of adversarial samples, with performance well-maintained.",
}
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<abstract>Despite the remarkable progress on Machine Reading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfortunately suffer from weak robustness against adversarial samples. To address this issue, we attempt to take sentence syntax as the leverage in the answer predicting process which previously only takes account of phrase-level semantics. Furthermore, to better utilize the sentence syntax and improve the robustness, we propose a Syntactic Leveraging Network, which is designed to deal with adversarial samples by exploiting the syntactic elements of a question. The experiment results indicate that our method is promising for improving the generalization and robustness of MRC models against the influence of adversarial samples, with performance well-maintained.</abstract>
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%0 Conference Proceedings
%T Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior
%A Wu, Bowen
%A Huang, Haoyang
%A Wang, Zongsheng
%A Feng, Qihang
%A Yu, Jingsong
%A Wang, Baoxun
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wu-etal-2019-improving
%X Despite the remarkable progress on Machine Reading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfortunately suffer from weak robustness against adversarial samples. To address this issue, we attempt to take sentence syntax as the leverage in the answer predicting process which previously only takes account of phrase-level semantics. Furthermore, to better utilize the sentence syntax and improve the robustness, we propose a Syntactic Leveraging Network, which is designed to deal with adversarial samples by exploiting the syntactic elements of a question. The experiment results indicate that our method is promising for improving the generalization and robustness of MRC models against the influence of adversarial samples, with performance well-maintained.
%R 10.18653/v1/D19-5807
%U https://aclanthology.org/D19-5807
%U https://doi.org/10.18653/v1/D19-5807
%P 53-57
Markdown (Informal)
[Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior](https://aclanthology.org/D19-5807) (Wu et al., 2019)
ACL