@inproceedings{jin-etal-2022-probing,
title = "Probing Script Knowledge from Pre-Trained Models",
author = "Jin, Zijia and
Zhang, Xingyu and
Yu, Mo and
Huang, Lifu",
editor = "Han, Wenjuan and
Zheng, Zilong and
Lin, Zhouhan and
Jin, Lifeng and
Shen, Yikang and
Kim, Yoon and
Tu, Kewei",
booktitle = "Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.umios-1.10",
doi = "10.18653/v1/2022.umios-1.10",
pages = "87--93",
abstract = "Adversarial attack of structured prediction models faces various challenges such as the difficulty of perturbing discrete words, the sentence quality issue, and the sensitivity of outputs to small perturbations. In this work, we introduce SHARP, a new attack method that formulates the black-box adversarial attack as a search-based optimization problem with a specially designed objective function considering sentence fluency, meaning preservation and attacking effectiveness. Additionally, three different searching strategies are analyzed and compared, , Beam Search, Metropolis-Hastings Sampling, and Hybrid Search. We demonstrate the effectiveness of our attacking strategies on two challenging structured prediction tasks: part-of-speech (POS) tagging and dependency parsing. Through automatic and human evaluations, we show that our method performs a more potent attack compared with pioneer arts. Moreover, the generated adversarial examples can be used to successfully boost the robustness and performance of the victim model via adversarial training.",
}
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<abstract>Adversarial attack of structured prediction models faces various challenges such as the difficulty of perturbing discrete words, the sentence quality issue, and the sensitivity of outputs to small perturbations. In this work, we introduce SHARP, a new attack method that formulates the black-box adversarial attack as a search-based optimization problem with a specially designed objective function considering sentence fluency, meaning preservation and attacking effectiveness. Additionally, three different searching strategies are analyzed and compared, , Beam Search, Metropolis-Hastings Sampling, and Hybrid Search. We demonstrate the effectiveness of our attacking strategies on two challenging structured prediction tasks: part-of-speech (POS) tagging and dependency parsing. Through automatic and human evaluations, we show that our method performs a more potent attack compared with pioneer arts. Moreover, the generated adversarial examples can be used to successfully boost the robustness and performance of the victim model via adversarial training.</abstract>
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%0 Conference Proceedings
%T Probing Script Knowledge from Pre-Trained Models
%A Jin, Zijia
%A Zhang, Xingyu
%A Yu, Mo
%A Huang, Lifu
%Y Han, Wenjuan
%Y Zheng, Zilong
%Y Lin, Zhouhan
%Y Jin, Lifeng
%Y Shen, Yikang
%Y Kim, Yoon
%Y Tu, Kewei
%S Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F jin-etal-2022-probing
%X Adversarial attack of structured prediction models faces various challenges such as the difficulty of perturbing discrete words, the sentence quality issue, and the sensitivity of outputs to small perturbations. In this work, we introduce SHARP, a new attack method that formulates the black-box adversarial attack as a search-based optimization problem with a specially designed objective function considering sentence fluency, meaning preservation and attacking effectiveness. Additionally, three different searching strategies are analyzed and compared, , Beam Search, Metropolis-Hastings Sampling, and Hybrid Search. We demonstrate the effectiveness of our attacking strategies on two challenging structured prediction tasks: part-of-speech (POS) tagging and dependency parsing. Through automatic and human evaluations, we show that our method performs a more potent attack compared with pioneer arts. Moreover, the generated adversarial examples can be used to successfully boost the robustness and performance of the victim model via adversarial training.
%R 10.18653/v1/2022.umios-1.10
%U https://aclanthology.org/2022.umios-1.10
%U https://doi.org/10.18653/v1/2022.umios-1.10
%P 87-93
Markdown (Informal)
[Probing Script Knowledge from Pre-Trained Models](https://aclanthology.org/2022.umios-1.10) (Jin et al., UM-IoS 2022)
ACL
- Zijia Jin, Xingyu Zhang, Mo Yu, and Lifu Huang. 2022. Probing Script Knowledge from Pre-Trained Models. In Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS), pages 87–93, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.