Phrase-level Adversarial Example Generation for Neural Machine Translation

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 is proved to be useful to alleviate this problem. Existing methods for adversarial example generation (AEG) are word-level or character-level. In this paper, we propose a phrase-level adversarial example generation (PAEG) method to enhance the robustness of the model. Our method leverages a gradient-based strategy to substitute phrases of vulnerable positions in the source input. 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 performance compared to previous methods.