Reviews: Ordered Memory

This paper presents a novel model design/algorithm for building compositional representations of sequences when (as in natural language or code) it is presumed that the sequences have salient latent structure that can be described as a binary tree. The method performs essentially at ceiling on two existing artificial datasets that were designed for this task, both of which have not been previously solved under comparable conditions. The method also performs reasonably well on a sentiment analysis task. Pros: The method is novel and solves a couple of prominent instances of an important open problem in deep learning for NLP and similar domains with latent structure: How to we build models that can efficiently learn and to build compositional representations using latent structure? This is interesting and likely to garner a reasonably large audience as a somewhat abstract/artificial result.