  Adversarial attacks in texts are mostly substitution-based methods that
replace words or characters in the original texts to achieve success attacks.
Recent methods use pre-trained language models as the substitutes generator.
While in Chinese, such methods are not applicable since words in Chinese
require segmentations first. In this paper, we propose a pre-train language
model as the substitutes generator using sentence-pieces to craft adversarial
examples in Chinese. The substitutions in the generated adversarial examples
are not characters or words but \textit{'pieces'}, which are more natural to
Chinese readers. Experiments results show that the generated adversarial
samples can mislead strong target models and remain fluent and semantically
preserved.
