  Can language models transform inputs to protect text classifiers against
adversarial attacks? In this work, we present ATINTER, a model that intercepts
and learns to rewrite adversarial inputs to make them non-adversarial for a
downstream text classifier. Our experiments on four datasets and five attack
mechanisms reveal that ATINTER is effective at providing better adversarial
robustness than existing defense approaches, without compromising task
accuracy. For example, on sentiment classification using the SST-2 dataset, our
method improves the adversarial accuracy over the best existing defense
approach by more than 4% with a smaller decrease in task accuracy (0.5% vs
2.5%). Moreover, we show that ATINTER generalizes across multiple downstream
tasks and classifiers without having to explicitly retrain it for those
settings. Specifically, we find that when ATINTER is trained to remove
adversarial perturbations for the sentiment classification task on the SST-2
dataset, it even transfers to a semantically different task of news
classification (on AGNews) and improves the adversarial robustness by more than
10%.
