In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

[EX Q]: 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.
[EX A]: en

[EX Q]: No hay contribucion cientifica interesante ya que es la aplicacion de una tecnologia ya conocida a un caso de estudio. A pesar de que el trabajo esta bien escrito tecnicamente y es facil de seguir no veo cual es la pregunta de investigacion que pretende responder. No se identifica un problema cientifico a solucionar y la propuesta es simple y poco original..
[EX A]: es

[EX Q]: UPDATE: The authors addressed all my concerns in the new version of the paper, so I raised my score and now recommend acceptance. -------------- This paper combines the recent progress in variational autoencoder and autoregressive density modeling in the proposed PixelVAE model. The paper shows that it can match the NLL performance of a PixelCNN with a PixelVAE that has a much shallower PixelCNN decoder. I think the idea of capturing the global structure with a VAE and modeling the local structure with a PixelCNN decoder makes a lot of sense and can prevent the blurry reconstruction/samples of VAE. I specially like the hierarchical image generation experiments. I have the following suggestions/concerns about the paper: 1) Is there any experiment showing that using the PixelCNN as the decoder of VAE will result in better disentangling of high-level factors of variations in the hidden code? For example, the authors can train a PixelVAE and VAE on MNIST with 2D hidden code and visualize the 2D hidden code for test images and color code each hidden code based on the digit and show that the digits have a better separation in the PixelVAE representation. A semi-supervised classification comparison between VAE and the PixelVAE will also significantly improve the quality of the paper. 2) A similar idea is also presented in a concurrent ICLR submission "Variational Lossy Autoencoder". It would be interesting to have a discussion included in the paper and compare these works. 3) The answer to the pre-review questions made the architecture details of the paper much more clear, but I still ask the authors to include the exact architecture details of all the experiments in the paper and/or open source the code. The clarity of the presentation is not satisfying and the experiments are difficult to reproduce. 4) As pointed out in my pre-review question, it would be great to include two sets of MNIST samples maybe in an appendix section. One with PixelCNN and the other with PixelVAE with the same pixelcnn depth to illustrate the hidden code in PixelVAE actually captures the global structure. I will gladly raise the score if the authors address my concerns.
[EX A]:
en