Published as a conference paper at ICLR 2018 UNSUPERVISED MACHINE TRANSLATION USING MONOLINGUAL CORPORA ONLY
Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.
The authors present an approach for unsupervised MT which uses a weighted loss function containing 3 components: (i) self reconstruction (ii) cross reconstruction and (iii) adversarial loss. The results are interesting (but perhaps less interesting than what is hinted in the abstract). 
1) In the abstract the authors mention that they achieve a BLEU score of 32.8 but omit the fact that this is only on Multi30K dataset and not on the more standard WMT datasets. At first glance, most people from the field would assume that this is on the WMT dataset. I request the authors to explicitly mention this in the abstract itself (there is clearly space and I don't see why this should be omitted)
2) In section 2.3, the authors talk about the Noise Model which is inspired by the standard Denoising Autoencoder setup.  While I understand the robustness argument in the case of AEs I am not convinced that the same applies to languages. Such random permutations can often completely alter the meaning of the sentence. The ablation test seems to suggest that this process helps. I read another paper which suggests that this noise does not help (which intuitively makes sense). I would like the authors to comment on this (of course, I am not asking you to compare with  the other paper but I am just saying that I have read contradicting observations - one which seems intuitive and the other does not).
3) How were the 3 lambdas in Equation 3 selected ? What ranges did you consider. The three loss terms seem to have very different ranges. How did you account for that?
4) Clarification: In section 2.5 what exactly do you mean by "as long as the two monolingual corpora exhibit strong structure in feature space." How do you quantify this ?
5) In section 4.1, can you please mention the exact number of sentences that you sampled from WMT'14. You mention that selected sentences from 15M random pairs but how many did you select ? The caption of one of the figure mentions that there were 10M sentences. Just want to confirm this.
6) The improvements are much better on the Multi30k dataset. I guess this is because this dataset has smaller sentences with smaller vocabulary. Can you provide a table comparing the average number of sentences and vocabulary size of the two datasets (Multi30k and WMT).
7) The ablation results are provided only for the Multi30k dataset. Can you provide similar results for the WMT dataset. Perhaps this would help in answering my query in point (2) above.
8) Can you also check the performance of a PBSMT system trained on 100K parallel sentences? Although NMT outperforms PBSMT when the data size is large, PBSMT might still be better suited for low resource settings.
9) There are some missing citations (already pointed by others in the forum) . Please add those.
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I have noted the clarifications posted by the authors. I still have concerns about a couple of things.  For example, I am still not convinced about the justification given for word order. I understand that empirically it works better but I don't get the intuition. Similarly, I don't get the argument about "strong structure in feature space". This is just a conjecture and it is very hard to measure it. I would request the authors to not emphasize on it or give a different more grounded intuition. 
I do acknowledge the efforts put in by the authors to address some of my comments and for that I would like to change my rating a bit.