@inproceedings{pereira-etal-2021-targeted,
title = "Targeted Adversarial Training for Natural Language Understanding",
author = "Pereira, Lis and
Liu, Xiaodong and
Cheng, Hao and
Poon, Hoifung and
Gao, Jianfeng and
Kobayashi, Ichiro",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.424",
doi = "10.18653/v1/2021.naacl-main.424",
pages = "5385--5393",
abstract = "We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding. The key idea is to introspect current mistakes and prioritize adversarial training steps to where the model errs the most. Experiments show that TAT can significantly improve accuracy over standard adversarial training on GLUE and attain new state-of-the-art zero-shot results on XNLI. Our code will be released upon acceptance of the paper.",
}
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%0 Conference Proceedings
%T Targeted Adversarial Training for Natural Language Understanding
%A Pereira, Lis
%A Liu, Xiaodong
%A Cheng, Hao
%A Poon, Hoifung
%A Gao, Jianfeng
%A Kobayashi, Ichiro
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F pereira-etal-2021-targeted
%X We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding. The key idea is to introspect current mistakes and prioritize adversarial training steps to where the model errs the most. Experiments show that TAT can significantly improve accuracy over standard adversarial training on GLUE and attain new state-of-the-art zero-shot results on XNLI. Our code will be released upon acceptance of the paper.
%R 10.18653/v1/2021.naacl-main.424
%U https://aclanthology.org/2021.naacl-main.424
%U https://doi.org/10.18653/v1/2021.naacl-main.424
%P 5385-5393
Markdown (Informal)
[Targeted Adversarial Training for Natural Language Understanding](https://aclanthology.org/2021.naacl-main.424) (Pereira et al., NAACL 2021)
ACL
- Lis Pereira, Xiaodong Liu, Hao Cheng, Hoifung Poon, Jianfeng Gao, and Ichiro Kobayashi. 2021. Targeted Adversarial Training for Natural Language Understanding. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5385–5393, Online. Association for Computational Linguistics.