  Unsupervised neural machine translation (UNMT) has recently attracted great
interest in the machine translation community. The main advantage of the UNMT
lies in its easy collection of required large training text sentences while
with only a slightly worse performance than supervised neural machine
translation which requires expensive annotated translation pairs on some
translation tasks. In most studies, the UMNT is trained with clean data without
considering its robustness to the noisy data. However, in real-world scenarios,
there usually exists noise in the collected input sentences which degrades the
performance of the translation system since the UNMT is sensitive to the small
perturbations of the input sentences. In this paper, we first time explicitly
take the noisy data into consideration to improve the robustness of the UNMT
based systems. First of all, we clearly defined two types of noises in training
sentences, i.e., word noise and word order noise, and empirically investigate
its effect in the UNMT, then we propose adversarial training methods with
denoising process in the UNMT. Experimental results on several language pairs
show that our proposed methods substantially improved the robustness of the
conventional UNMT systems in noisy scenarios.
