Expanding Scope: Adapting English Adversarial Attacks to Chinese
This work addresses the need for robust NLP solutions in non-English languages, though it is incremental as it adapts existing methods to a new domain.
The paper tackles the problem of adapting English adversarial attack methods to Chinese, showing that with proper segmentation and linguistic constraints, these methods can generate high-quality adversarial examples that achieve high fluency and semantic consistency.
Recent studies have revealed that NLP predictive models are vulnerable to adversarial attacks. Most existing studies focused on designing attacks to evaluate the robustness of NLP models in the English language alone. Literature has seen an increasing need for NLP solutions for other languages. We, therefore, ask one natural question: whether state-of-the-art (SOTA) attack methods generalize to other languages. This paper investigates how to adapt SOTA adversarial attack algorithms in English to the Chinese language. Our experiments show that attack methods previously applied to English NLP can generate high-quality adversarial examples in Chinese when combined with proper text segmentation and linguistic constraints. In addition, we demonstrate that the generated adversarial examples can achieve high fluency and semantic consistency by focusing on the Chinese language's morphology and phonology, which in turn can be used to improve the adversarial robustness of Chinese NLP models.