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Improving the robustness of neural machine translation models has been an active area of research in recent years. In this paper, we propose a novel training approach, called adversarial training with feature-based augmentation (ATFA), to enhance the robustness of neural machine translation models against character-level and word-level perturbations. The key idea of ATFA is to generate augmented training data by applying various types of perturbations to the source and target sentences in the training corpus, and then use the augmented data to train a neural machine translation model with adversarial training. Experimental results demonstrate that our approach can significantly improve the robustness of neural machine translation models on various benchmark datasets.
This paper proposes a novel training approach called adversarial training with feature-based augmentation (ATFA) to enhance the robustness of neural machine translation models against perturbations. ATFA generates augmented training data by perturbing the source and target sentences and uses it to train a neural machine translation model. Experimental results show that ATFA significantly improves the model's robustness on benchmark datasets.