Towards Evaluating the Robustness of Chinese BERT Classifiers

Boxin Wang; Boyuan Pan; Xin Li; Bo Li

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arXiv · 2004.03742

Towards Evaluating the Robustness of Chinese BERT Classifiers

Boxin Wang·Boyuan Pan·Xin Li·Bo Li

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Abstract

Recent advances in large-scale language representation models such as BERT have improved the state-of-the-art performances in many NLP tasks. Meanwhile, character-level Chinese NLP models, including BERT for Chinese, have also demonstrated that they can outperform the existing models. In this paper, we show that, however, such BERT-based models are vulnerable under character-level adversarial attacks. We propose a novel Chinese char-level attack method against BERT-based classifiers. Essentially, we generate "small" perturbation on the character level in the embedding space and guide the character substitution procedure. Extensive experiments show that the classification accuracy on a Chinese news dataset drops from 91.8% to 0% by manipulating less than 2 characters on average based on the proposed attack. Human evaluations also confirm that our generated Chinese adversarial examples barely affect human performance on these NLP tasks.

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BibTeX RIS

Boxin Wang, Boyuan Pan, Xin Li, Bo Li. 2020-04-07. Towards Evaluating the Robustness of Chinese BERT Classifiers. https://arxiv.org/abs/2004.03742

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