Expanding Scope: Adapting English Adversarial Attacks to Chinese
Hanyu Liu Chengyuan Cai Yanjun Qi
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.