Open the Boxes of Words: Incorporating Sememes into Textual Adversarial Attack

Adversarial attack is carried out to reveal the vulnerability of deep neural networks. Word substitution is a class of effective adversarial textual attack method, which has been extensively explored. However, all existing studies utilize word embeddings or thesauruses to find substitutes. In this paper, we incorporate sememes, the minimum semantic units, into adversarial attack. We propose an efficient sememe-based word substitution strategy and integrate it into a genetic attack algorithm. In experiments, we employ our attack method to attack LSTM and BERT on both Chinese and English sentiment analysis as well as natural language inference benchmark datasets. Experimental results demonstrate our model achieves better attack success rates and less modification than the baseline methods based on word embedding or synonym. Furthermore, we find our attack model can bring more robustness enhancement to the target model with adversarial training.