Robust Neural Machine Translation with Doubly Adversarial Inputs
This addresses robustness issues in NMT for translation tasks, but it is incremental as it builds on existing adversarial methods.
The paper tackles the problem of neural machine translation (NMT) models being vulnerable to noisy input perturbations by proposing a doubly adversarial approach that attacks with adversarial source examples and defends with adversarial target inputs, resulting in improvements of 2.8 and 1.6 BLEU points on clean benchmarks and higher robustness on noisy data.
Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the 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 inputs to improve its robustness against the adversarial source inputs.For the generation of adversarial inputs, we propose a gradient-based method to craft adversarial examples informed by the translation loss over the clean inputs.Experimental results on Chinese-English and English-German translation tasks demonstrate that our approach achieves significant improvements ($2.8$ and $1.6$ BLEU points) over Transformer on standard clean benchmarks as well as exhibiting higher robustness on noisy data.