  While end-to-end neural machine translation (NMT) has achieved impressive
progress, noisy input usually leads models to become fragile and unstable.
Generating adversarial examples as the augmented data has been proved to be
useful to alleviate this problem. Existing methods for adversarial example
generation (AEG) are word-level or character-level, which ignore the ubiquitous
phrase structure. In this paper, we propose a Phrase-level Adversarial Example
Generation (PAEG) framework to enhance the robustness of the translation model.
Our method further improves the gradient-based word-level AEG method by
adopting a phrase-level substitution strategy. We verify our method on three
benchmarks, including LDC Chinese-English, IWSLT14 German-English, and WMT14
English-German tasks. Experimental results demonstrate that our approach
significantly improves translation performance and robustness to noise compared
to previous strong baselines.
