text: This paper presents an analysis of the communication systems that arose when neural network based agents played simple referential games.  The set up is that a speaker and a listener engage in a game where both can see a set of possible referents (either represented symbolically in terms of features, or represented as simple images) and the speaker produces a message consisting of a sequence of numbers while the listener has to make the choice of which referent the speaker intends.  This is a set up that has been used in a large amount of previous work,  and the authors summarize some of this work.  The main novelty in this paper is the choice of models to be used by speaker and listener,  which are based on LSTMs and convolutional neural networks.  The results show that the agents generate effective communication systems,  and some analysis is given of the extent to which these communications systems develop compositional properties  – a question that is currently being explored in the literature on language creation. This is an interesting question,  and it is nice to see worker playing modern neural network models to his question and exploring the properties of the solutions of the phone.  However, there are also a number of issues with the work. 1. One of the key question is the extent to which the constructed communication systems demonstrate compositionality.  The authors note that there is not a good quantitative measure of this.  However, this is been the topic of much research of the literature and language evolution.  This work has resulted in some measures that could be applied here,  see for example Carr et al. (2016): http://www.research.ed.ac.uk/portal/files/25091325/Carr_et_al_2016_Cognitive_Science.pdf 2. In general the results occurred be more quantitative.  In section 3.3.2 it would be nice to see statistical tests used to evaluate the claims.  Minimally I think it is necessary to calculate a null distribution for the statistics that are reported. 3. As noted above the main novelty of this work is the use of contemporary network models.  One of the advantages of this is that it makes it possible to work with more complex data stimuli, such as images.  However, unfortunately the image example that is used is still very artificial being based on a small set of synthetically generated images. Overall, I see this as an interesting piece of work that may be of interest to researchers exploring questions around language creation and language evolution,  but I think the results require more careful analysis and the novelty is relatively limited, at least in the way that the results are presented here.