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        <title>BAE: BERT-based Adversarial Examples for Text Classification</title>
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    <abstract>Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context and unnaturally complex token replacements, which are easily identifiable by humans. We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging the BERT-MLM to generate alternatives for the masked tokens. Through automatic and human evaluations, we show that BAE performs a stronger attack, in addition to generating adversarial examples with improved grammaticality and semantic coherence as compared to prior work.</abstract>
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%0 Conference Proceedings
%T BAE: BERT-based Adversarial Examples for Text Classification
%A Garg, Siddhant
%A Ramakrishnan, Goutham
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F garg-ramakrishnan-2020-bae
%X Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context and unnaturally complex token replacements, which are easily identifiable by humans. We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging the BERT-MLM to generate alternatives for the masked tokens. Through automatic and human evaluations, we show that BAE performs a stronger attack, in addition to generating adversarial examples with improved grammaticality and semantic coherence as compared to prior work.
%R 10.18653/v1/2020.emnlp-main.498
%U https://aclanthology.org/2020.emnlp-main.498
%U https://doi.org/10.18653/v1/2020.emnlp-main.498
%P 6174-6181

Markdown (Informal)

[BAE: BERT-based Adversarial Examples for Text Classification](https://aclanthology.org/2020.emnlp-main.498) (Garg & Ramakrishnan, EMNLP 2020)

• BAE: BERT-based Adversarial Examples for Text Classification (Garg & Ramakrishnan, EMNLP 2020)

ACL

• Siddhant Garg and Goutham Ramakrishnan. 2020. BAE: BERT-based Adversarial Examples for Text Classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6174–6181, Online. Association for Computational Linguistics.