Robust Neural Machine Translation with Doubly Adversarial Inputs

Neural machine translation (NMT) suffers from the vulnerability to noisy perturbations in the input, which can cause a model trained on the clean data to behave abnormally on the noisy input. We propose an approach to improving the robustness of NMT models, which consists of two parts: (1) attack the translation model with adversarial source examples; (2) defend the translation model with adversarial target input to be robust against adversarial source input. For the generation of adversarial input, we propose to use a gradient-based method to craft adversarial examples that are advised by the translation loss in NMT based on the clean input. Experimental results on Chinese-English and English-German translation tasks demonstrate that our approach achieves significant improvements on the standard clean data and performs robustness on the noisy data.

• Natural Language Processing

• Machine Translation