text: The authors present a model for unsupervised NMT which requires no parallel corpora between the two languages of interest. While the results are interesting I find very few original ideas in this paper. Please find my comments/questions/suggestions below: 1) The authors mention that there are 3 important aspects in which their model differs from a standard NMT architecture. All the 3 differences have been adapted from existing works. The authors clearly acknowledge and cite the sources. Even sharing the encoder using cross lingual embeddings has been explored in the context of multilingual NER (please see https://arxiv.org/abs/1607.00198). Because of this I find the paper to be a bit lacking on the novelty quotient. Even backtranslation has been used successfully in the past (as acknowledged by the authors). Unsupervised MT in itself is not a new idea (again clearly acknowledged by the authors). 2) I am not very convinced about the idea of denoising. Specifically, I am not sure if it will work for arbitrary language pairs. In fact, I think there is a contradiction even in the way the authors write this. On one hand, they want to "learn the internal structure of the languages involved" and on the other hand they deliberately corrupt this structure by adding noise. This seems very counter-intuitive and in fact the results in Table 1 suggest that it leads to a drop in performance. I am not very sure that the analogy with autoencoders holds in this case. 3) Following up on the above question, the authors mention that "We emphasize, however, that it is not possible to use backtranslation alone without denoising". Again, if denoising itself leads to a drop in the performance as compared to the nearest neighbor baseline then why use backtranslation in conjunction with denoising and not in conjunction with the baseline itself. 4) This point is more of a clarification and perhaps due to my lack of understanding. Backtranslation to generate a pseudo corpus makes sense only after the model has achieved a certain (good) performance. Can you please provide details of how long did you train the model (with denoising?) before producing the backtranslations ? 5) The authors mention that 100K parallel sentences may be insufficient for training a NMT system. However, this size may be decent enough for  a PBSMT system. It would be interesting to see the performance of a PBSMT system trained on 100K parallel sentences. 6) How did you arrive at the beam size of 12 ? Was this a hyperparameter? Just curious. 7) The comparable NMT set up is not very clear. Can you please explain it in detail ? In the same paragraph, what exactly do you mean by "the supervised system in this paper is relatively small?"