  Though model robustness has been extensively studied in language
understanding, the robustness of Seq2Seq generation remains understudied. In
this paper, we conduct the first quantitative analysis on the robustness of
pre-trained Seq2Seq models. We find that even current SOTA pre-trained Seq2Seq
model (BART) is still vulnerable, which leads to significant degeneration in
faithfulness and informativeness for text generation tasks. This motivated us
to further propose a novel adversarial augmentation framework, namely AdvSeq,
for generally improving faithfulness and informativeness of Seq2Seq models via
enhancing their robustness. AdvSeq automatically constructs two types of
adversarial augmentations during training, including implicit adversarial
samples by perturbing word representations and explicit adversarial samples by
word swapping, both of which effectively improve Seq2Seq robustness. Extensive
experiments on three popular text generation tasks demonstrate that AdvSeq
significantly improves both the faithfulness and informativeness of Seq2Seq
generation under both automatic and human evaluation settings.
