Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side semantic meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite translation directions with a joint loss function, which combines the target-side attack and the source-side semantic similarity constraint. The results from our experiments across three different language pairs and two evaluation metrics show that these adversarial samples improve the model robustness.

翻译：已知神经机器翻译模型受到吵闹投入的影响。 要使模型坚固,我们产生攻击模型的对抗性增强样本,同时保存源端语义含义。 为了生成这种样本,我们提议一个经过双重培训的结构,将两个相反翻译方向的NMT模型与联合损失函数对齐,将目标端攻击和源端语义相似性制约结合起来。 我们通过三个不同语言配对和两个评价指标的实验结果显示,这些对抗性样本改善了模型的坚固性。