text: Authors proposed a neural network based machine translation method between two programming languages.  The model is based on both source/target syntax trees and performs an attentional encoder-decoder style network over the tree structure. The new things in the paper are the task definition and using the tree-style network in both encoder and decoder.  Although each structure of encoder/decoder/attention network is based on the application of some well-known components,  unfortunately, the paper pays much space to describe them.  On the other hand, the whole model structure looks to be easily generalized to other tree-to-tree tasks and might have some potential to contribute this kind of problems. In experimental settings, there are many shortages of the description.  First, it is unclear that what the linearization method of the syntax tree is,  which could affect the final model accuracy.  Second, it is also unclear what the method to generate train/dev/test data is.  Are those generated completely randomly?  If so, there could be many meaningless (e.g., inexecutable) programs in each dataset.  What is the reasonableness of training such kind of data, or are they already avoided from the data?  Third, the evaluation metrics "token/program accuracy" looks insufficient about measuring the correctness of the program  because it has sensitivity about meaningless differences between identifier names and some local coding styles. Authors also said that CoffeeScript has a succinct syntax and Javascript has a verbose one without any agreement about what the syntax complexity is.  Since any CoffeeScript programs can be compiled into the corresponding Javascript programs,  we should assume that CoffeeScript is the only subset of Javascript (without physical difference of syntax),  and this translation task may never capture the whole tendency of Javascript.  In addition, authors had generated the source CoffeeScript codes,  which seems that this task is only one of "synthetic" task and no longer capture any real world's programs. If authors were interested in the tendency of real program translation task, they should arrange the experiment by collecting parallel corpora between some unrelated programming languages using resources in the real world. Global attention mechanism looks somewhat not suitable for this task.  Probably we can suppress the range of each attention by introducing some prior knowledge about syntax trees (e.g., only paying attention to the descendants in a specific subtree). Suggestion: After capturing the motivation of the task, I suspect that the traditional tree-to-tree (also X-to-tree) "statistical" machine translation methods still can also work correctly in this task.  The traditional methods are basically based on the rule matching, which constructs a target tree by selecting source/target subtree pairs and arranging them according to the actual connections between each subtree in the source tree.  This behavior might be suitable to transform syntax trees while keeping their whole structure, and also be able to treat the OOV (e.g., identifier names) problem by a trivial modification.  Although it is not necessary, it would like to apply those methods to this task as another baseline if authors are interested in.