  Recent works have shown that powerful pre-trained language models (PLM) can
be fooled by small perturbations or intentional attacks. To solve this issue,
various data augmentation techniques are proposed to improve the robustness of
PLMs. However, it is still challenging to augment semantically relevant
examples with sufficient diversity. In this work, we present Virtual Data
Augmentation (VDA), a general framework for robustly fine-tuning PLMs. Based on
the original token embeddings, we construct a multinomial mixture for
augmenting virtual data embeddings, where a masked language model guarantees
the semantic relevance and the Gaussian noise provides the augmentation
diversity. Furthermore, a regularized training strategy is proposed to balance
the two aspects. Extensive experiments on six datasets show that our approach
is able to improve the robustness of PLMs and alleviate the performance
degradation under adversarial attacks. Our codes and data are publicly
available at \textcolor{blue}{\url{https://github.com/RUCAIBox/VDA}}.
