Paper:
We introduce a neural network that represents sentences by composing their words according to induced binary parse trees. We use Tree-LSTM as our composition function, applied along a tree structure found by a fully differentiable natural language chart parser. Our model simultaneously optimises both the composition function and the parser, thus eliminating the need for externally-provided parse trees which are normally required for Tree-LSTM. It can therefore be seen as a tree-based RNN that is unsupervised with respect to the parse trees. As it is fully differentiable, our model is easily trained with an off-the-shelf gradient descent method and backpropagation. We demonstrate that it achieves better performance compared to various supervised Tree-LSTM architectures on a textual entailment task and a reverse dictionary task. Finally, we show how performance can be improved with an attention mechanism which fully exploits the parse chart, by attending over all possible subspans of the sentence.

Review:
The paper presents a model titled the "unsupervised tree-LSTM," in which the authors mash up a dynamic-programming chart and a recurrent neural network. As far as I can glean, the topology of the neural network is constructed using the chart of a CKY parser. When combining different constituents, an energy function is computed (equation 6) and the resulting energies are passed through a softmax. The architecture achieves impressive results on two tasks: SNLI and the reverse dictionary of Hill et al. (2016).
Overall, I found the paper deeply uninspired. The authors downplay the similarity of their paper to that of Le and Zuidema (2015), which  I did not appreciate. It's true that Le and Zuidema take a parse forest from an existing parser, but it still contains an exponential number of trees, as does the work in here. Note that exposition in Le and Zuidema (2015) discusses the pruned case as well, i.e., a compete parse forest. The authors of this paper simply write "Le and Zuidema (2015) propose a model that takes as input a parse forest from an external parser, in order to deal with uncertainty." I would encourage the authors to revisit Le and Zuidema (2015), especially section 3.2, and consider the technical innovations over the existing work. I believe the primary difference (other using an LSTM instead of a convnet) is to replace max-pooling with softmax-pooling. Do these two architectural changes matter? The experiments offer no empirical comparison. In short, the insight of having an end-to-end differentiable function based on a dynamic-programming chart is pretty common -- the idea is in the air. The authors provide yet another instantiation of such an approach, but this time with an LSTM. 
The technical exposition is also relatively poor. The authors could have expressed their network using a clean recursion, following the parse chart, but opted not to, and, instead,  provided a round-about explanation in English. Thus, despite the strong results, I would not like to see this work in the proceedings, due to the lack of originality and poor technical discussion. If the paper were substantially cleaned-up, I would be willing to increase my rating.