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Unsupervised neural machine translation (UNMT) has recently attracted greatinterest in the machine translation community. The main advantage of the UNMTlies in its easy collection of required large training text sentences whilewith only a slightly worse performance than supervised neural machinetranslation which requires expensive annotated translation pairs on sometranslation tasks. In most studies, the UMNT is trained with clean data withoutconsidering its robustness to the noisy data. However, in real-world scenarios,there usually exists noise in the collected input sentences which degrades theperformance of the translation system since the UNMT is sensitive to the smallperturbations of the input sentences. In this paper, we first time explicitlytake the noisy data into consideration to improve the robustness of the UNMTbased systems. First of all, we clearly defined two types of noises in trainingsentences, i.e., word noise and word order noise, and empirically investigateits effect in the UNMT, then we propose adversarial training methods withdenoising process in the UNMT. Experimental results on several language pairsshow that our proposed methods substantially improved the robustness of theconventional UNMT systems in noisy scenarios.

Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training