  Attacking Neural Machine Translation models is an inherently combinatorial
task on discrete sequences, solved with approximate heuristics. Most methods
use the gradient to attack the model on each sample independently. Instead of
mechanically applying the gradient, could we learn to produce meaningful
adversarial attacks ? In contrast to existing approaches, we learn to attack a
model by training an adversarial generator based on a language model. We
propose the Masked Adversarial Generation (MAG) model, that learns to perturb
the translation model throughout the training process. The experiments show
that it improves the robustness of machine translation models, while being
faster than competing methods.
