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Recent studies have revealed that NLP predictive models are vulnerable toadversarial attacks. Most existing studies focused on designing attacks toevaluate the robustness of NLP models in the English language alone. Literaturehas seen an increasing need for NLP solutions for other languages. We,therefore, ask one natural question: whether state-of-the-art (SOTA) attackmethods generalize to other languages. This paper investigates how to adaptSOTA adversarial attack algorithms in English to the Chinese language. Ourexperiments show that attack methods previously applied to English NLP cangenerate high-quality adversarial examples in Chinese when combined with propertext segmentation and linguistic constraints. In addition, we demonstrate thatthe generated adversarial examples can achieve high fluency and semanticconsistency by focusing on the Chinese language's morphology and phonology,which in turn can be used to improve the adversarial robustness of Chinese NLPmodels.

Expanding Scope: Adapting English Adversarial Attacks to Chinese