@inproceedings{hsieh-etal-2019-robustness,
title = "On the Robustness of Self-Attentive Models",
author = "Hsieh, Yu-Lun and
Cheng, Minhao and
Juan, Da-Cheng and
Wei, Wei and
Hsu, Wen-Lian and
Hsieh, Cho-Jui",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1147",
doi = "10.18653/v1/P19-1147",
pages = "1520--1529",
abstract = "This work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks. We also propose a novel attack algorithm for generating more natural adversarial examples that could mislead neural models but not humans. Experimental results show that, compared to recurrent neural models, self-attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explanations for their superior robustness to support our claims.",
}
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<abstract>This work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks. We also propose a novel attack algorithm for generating more natural adversarial examples that could mislead neural models but not humans. Experimental results show that, compared to recurrent neural models, self-attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explanations for their superior robustness to support our claims.</abstract>
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%0 Conference Proceedings
%T On the Robustness of Self-Attentive Models
%A Hsieh, Yu-Lun
%A Cheng, Minhao
%A Juan, Da-Cheng
%A Wei, Wei
%A Hsu, Wen-Lian
%A Hsieh, Cho-Jui
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F hsieh-etal-2019-robustness
%X This work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks. We also propose a novel attack algorithm for generating more natural adversarial examples that could mislead neural models but not humans. Experimental results show that, compared to recurrent neural models, self-attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explanations for their superior robustness to support our claims.
%R 10.18653/v1/P19-1147
%U https://aclanthology.org/P19-1147
%U https://doi.org/10.18653/v1/P19-1147
%P 1520-1529
Markdown (Informal)
[On the Robustness of Self-Attentive Models](https://aclanthology.org/P19-1147) (Hsieh et al., ACL 2019)
ACL
- Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, and Cho-Jui Hsieh. 2019. On the Robustness of Self-Attentive Models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1520–1529, Florence, Italy. Association for Computational Linguistics.