However, there are many points that need to be address before this paper is ready for publication.
1) Crucial information is missing Can you flesh out more clearly how training and decoding happen in your training framework? I found out that the equations do not completely describe the approach. It might be useful to use a couple of examples to make your approach clearer.
Also, how is the montecarlo sampling done? 2) Organization The paper is not very well organized. For example, results are broken into several subsections, while they’d better be presented together.  The organization of the tables is very confusing. Table 7 is referred before table 6. This made it difficult to read the results.
3) Inconclusive results: After reading the results section, it’s difficult to draw conclusions when, as the authors point out in their comparisons, this can be explained by the total size of the corpus involved in their methods (621  ). 4) Not so useful information: While I appreciate the fleshing out of the assumptions, I find that dedicating a whole section of the paper plus experimental results is a lot of space. - General Discussion: Other: 578:  We observe that word-level models tend to have lower valid loss compared with sentence- level methods….
Is it valid to compare the loss from two different loss functions?
Sec 3.2, the notations are not clear. What does script(Y) means?
How do we get p(y|x)? this is never explained Eq 7 deserves some explanation, or better removed.
320: What approach did you use? You should talk about that here 392 : Do you mean 2016?
Nitty-gritty: 742  : import => important 772  : inline citation style 778: can significantly outperform 275: Assumption 2 needs to be rewritten … a target sentence y from x should be close to that from its counterpart z.
4) Not so useful information: While I appreciate the fleshing out of the assumptions, I find that dedicating a whole section of the paper plus experimental results is a lot of space.