PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation
Juncheng Wan, Jian Yang, Shuming Ma, Dongdong Zhang, Weinan Zhang, Yong Yu, Zhoujun Li
Abstract
While end-to-end neural machine translation (NMT) has achieved impressive progress, noisy input usually leads models to become fragile and unstable. Generating adversarial examples as the augmented data has been proved to be useful to alleviate this problem. Existing methods for adversarial example generation (AEG) are word-level or character-level, which ignore the ubiquitous phrase structure. In this paper, we propose a Phrase-level Adversarial Example Generation (PAEG) framework to enhance the robustness of the translation model. Our method further improves the gradient-based word-level AEG method by adopting a phrase-level substitution strategy. We verify our method on three benchmarks, including LDC Chinese-English, IWSLT14 German-English, and WMT14 English-German tasks. Experimental results demonstrate that our approach significantly improves translation performance and robustness to noise compared to previous strong baselines.- Anthology ID:
- 2022.coling-1.451
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5085–5097
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.451
- DOI:
- Bibkey:
- Cite (ACL):
- Juncheng Wan, Jian Yang, Shuming Ma, Dongdong Zhang, Weinan Zhang, Yong Yu, and Zhoujun Li. 2022. PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5085–5097, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (Wan et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.451.pdf
Export citation
@inproceedings{wan-etal-2022-paeg,
title = "{PAEG}: Phrase-level Adversarial Example Generation for Neural Machine Translation",
author = "Wan, Juncheng and
Yang, Jian and
Ma, Shuming and
Zhang, Dongdong and
Zhang, Weinan and
Yu, Yong and
Li, Zhoujun",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.451",
pages = "5085--5097",
abstract = "While end-to-end neural machine translation (NMT) has achieved impressive progress, noisy input usually leads models to become fragile and unstable. Generating adversarial examples as the augmented data has been proved to be useful to alleviate this problem. Existing methods for adversarial example generation (AEG) are word-level or character-level, which ignore the ubiquitous phrase structure. In this paper, we propose a Phrase-level Adversarial Example Generation (PAEG) framework to enhance the robustness of the translation model. Our method further improves the gradient-based word-level AEG method by adopting a phrase-level substitution strategy. We verify our method on three benchmarks, including LDC Chinese-English, IWSLT14 German-English, and WMT14 English-German tasks. Experimental results demonstrate that our approach significantly improves translation performance and robustness to noise compared to previous strong baselines.",
}
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%0 Conference Proceedings %T PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation %A Wan, Juncheng %A Yang, Jian %A Ma, Shuming %A Zhang, Dongdong %A Zhang, Weinan %A Yu, Yong %A Li, Zhoujun %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F wan-etal-2022-paeg %X While end-to-end neural machine translation (NMT) has achieved impressive progress, noisy input usually leads models to become fragile and unstable. Generating adversarial examples as the augmented data has been proved to be useful to alleviate this problem. Existing methods for adversarial example generation (AEG) are word-level or character-level, which ignore the ubiquitous phrase structure. In this paper, we propose a Phrase-level Adversarial Example Generation (PAEG) framework to enhance the robustness of the translation model. Our method further improves the gradient-based word-level AEG method by adopting a phrase-level substitution strategy. We verify our method on three benchmarks, including LDC Chinese-English, IWSLT14 German-English, and WMT14 English-German tasks. Experimental results demonstrate that our approach significantly improves translation performance and robustness to noise compared to previous strong baselines. %U https://aclanthology.org/2022.coling-1.451 %P 5085-5097
Markdown (Informal)
[PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation](https://aclanthology.org/2022.coling-1.451) (Wan et al., COLING 2022)
- PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation (Wan et al., COLING 2022)
ACL
- Juncheng Wan, Jian Yang, Shuming Ma, Dongdong Zhang, Weinan Zhang, Yong Yu, and Zhoujun Li. 2022. PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5085–5097, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.