Task: Language Identification
Input (Spanish, English): The paper presents a general approach to modeling for natural language understanding problems with two distinct textual inputs (such as a question and a source text) that can be aligned in some way. In the approach, soft attention is first used to derive alignments between the tokens of the two texts, then a comparison function uses the resulting alignments (represented as pairs of attention queries and attention results) to derive a representations that are aggregated by CNN into a single vector from which an output can be computed. The paper both presents this as an overall modeling strategy that can be made to work quite well, and offers a detailed empirical analysis of the comparison component of the model.  This work is timely. Language understanding problems of this kind are a major open issue in NLP, and are just at the threshold of being addressable with representation learning methods. The work presents a general approach which is straightforward and reasonable, and shows that it can yield good results. The work borders on incremental (relative to their earlier work or that of Parikh et al.), but it contributes in enough substantial ways that I'd strongly recommend acceptance.  Detail:  - The model, at least as implemented for the problems with longer sequences (everything but SNLI), is not sensitive to word order. It is empirically competitive, but this insensitivity places a strong upper bound on its performance. The paper does make this clear, but it seems salient enough to warrant a brief mention in the introduction or discussion sections. - If I understand correctly, your attention strategy is based more closely on the general/bilinear strategy of Luong et al. '15 than it is on the earlier Bahdanau work. You should probably cite the former (or some other more directly relevant reference for that strategy). - Since the NTN risks overfitting because of its large number of parameters, did you try using a version with input dimension l and a smaller output dimension m (so an l*l*m tensor)? - You should probably note that SubMultNN looks a lot like the strategy for *sentence*-level matching in the Lili Mou paper you cite. - Is there a reason you use the same parameters for preprocessing the question and answer in (1)? These could require different things to be weighted highly.
Output (English): en