text: This paper describes how to use a set of sentences in the source language mapped to another set of sentences in the target sentences instead of using single sentence to sentence samples.  The paper claims superior results using the described method. Overall, there are a few problems with the paper.  1) The arguments for using clusters instead of single sentences are questionable.   The paper claims several times that MLE training for NMT faces over-training (or data sparsity)  -- while that can be true depending on the corpus and model used, there are well-known remedies for that, for example regularization via dropout (almost everybody uses that).  It is not clear why that is not used or at least compared to the method presented.  2) The writing of the paper is often unclear (and sometimes grammatically wrong, typos etc. but that aside),  there are some made up words/concepts (What is 'Golden Centroid Augmentation" or "Model Centroid Augmentation"?  The reason for attention is not to better memorize input information,  it is to be able to attend to certain regions in the input.  The reason to use RL is to focus on optimizing directly for BLEU score or other metrics instead of likelihood  but not for improving on the train/test loss discrepancy.  There are lots more examples of unclear statements in this paper  -- it should be heavily improved.  3) Section 3 and 4 are very hard/impossible to understand,  it is not clear how the formulas help the reader to better understand the concept in any way.  5) The results presented in this paper given the complexity of the method are just not great  -- for example, WMT en-de is 21.3 BLEU reported by you while much older papers report for example 24.67 BLEU (Google's Neural Machine Translation System)  -- why not first try to get to state-of-the-art with already published methods and then try to improve on top of that? .   6) Finally, what is missing most is simply why a much simpler method (just generate some data using a trained system and use that as additional training data, with details on how much etc.) -- is not directly compared to this very complicated looking method.