text: Overall, the paper is well-written  and the proposed model is quite intuitive.  Specifically, the idea is to represent entailment as a product of continuous functions over possible worlds.  Specifically, the idea is to generate possible worlds, and compute the functions that encode entailment in those worlds.  The functions themselves are designed as tree neural networks to take advantage of logical structure.  Several different encoding benchmarks of the entailment task are designed to compare against the performance of the proposed model, using a newly created dataset.  The results seem very impressive with > 99% accuracy on tests sets. One weakness with the paper was that it was only tested on 1 dataset.  Also, should some form of cross-validation be applied to smooth out variance in the evaluation results.  I am not sure if there are standard "shared" datasets for this task,  which would make the results much stronger. Also how about the tradeoff, i.e., does training time significantly increase when we "imagine" more worlds.  Also, in general, a discussion on the efficiency of training the proposed model as compared to TreeNN would be helpful. The size of the world vectors, I would believe is quite important,  so maybe a more detailed analysis on how this was chosen is important to replicate the results. This problem, I think, is quite related to model counting.  There has been a lot of work on model counting.  a discussion on how this relates to those lines of work would be interesting.