  Despite the strong performance of current NLP models, they can be brittle
against adversarial attacks. To enable effective learning against adversarial
inputs, we introduce the use of rationale models that can explicitly learn to
ignore attack tokens. We find that the rationale models can successfully ignore
over 90% of attack tokens. This approach leads to consistent sizable
improvements ($\sim$10%) over baseline models in robustness on three datasets
for both BERT and RoBERTa, and also reliably outperforms data augmentation with
adversarial examples alone. In many cases, we find that our method is able to
close the gap between model performance on a clean test set and an attacked
test set and hence reduce the effect of adversarial attacks.
