  Pre-trained language models (PLMs) fail miserably on adversarial attacks. To
improve the robustness, adversarial data augmentation (ADA) has been widely
adopted, which attempts to cover more search space of adversarial attacks by
adding the adversarial examples during training. However, the number of
adversarial examples added by ADA is extremely insufficient due to the
enormously large search space. In this work, we propose a simple and effective
method to cover much larger proportion of the attack search space, called
Adversarial Data Augmentation with Mixup (MixADA). Specifically, MixADA
linearly interpolates the representations of pairs of training examples to form
new virtual samples, which are more abundant and diverse than the discrete
adversarial examples used in conventional ADA. Moreover, to evaluate the
robustness of different models fairly, we adopt a challenging setup, which
dynamically generates new adversarial examples for each model. In the text
classification experiments of BERT and RoBERTa, MixADA achieves significant
robustness gains under two strong adversarial attacks and alleviates the
performance degradation of ADA on the original data. Our source codes will be
released to support further explorations.
