Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
Table 2: Original accuracy of target models on standard test sets.
['[EMPTY]', '[BOLD] WordCNN', '[BOLD] WordLSTM', '[BOLD] BERT']
We then generate adversarial examples that are semantically similar to the test set to attack the trained models and make them generate different results.