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].

Q: This work describes a gated attention-based recurrent neural network method for reading comprehension and question answering. This method employs a self-matching attention technique to counterbalance the limited context knowledge of gated attention-based recurrent neural networks when processing passages. Finally, authors use pointer networks  with signals from the question attention-based vector to predict the beginning and ending of the answer. Experimental results with the SQuAD dataset offer state-of-the-art performance compared with several recent approaches.   The paper is well-written, structured and explained. As far as I know, the mathematics look also good. In my opinion, this is a very interesting work which may be useful for the question answering community.  I was wondering if the authors have plans to release the code of this approach. From that perspective, I miss a bit of information about the technology used for the implementation (theano, CUDA, CuDNN...), which may be useful for readers.  I would appreciate if authors could perform a test of statistical significance of the results. That would highlight even more the quality of your results.  Finally, I know that the space may be a constraint, but an evaluation including some additional dataset would validate more your work.

A: en
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Q: Buena práctica para mejor la calidad en la ingeniería de requisitos  - En el resumen no debería haber teoría - Debería haber un caso de estudio para ver mejor la aplicación de la metodología

A: es
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Q: This paper studies neural models that can be applied to set-structured inputs and thus require permutation invariance or equivariance. After a first section that introduces necessary and sufficient conditions for permutation invariance/equivariance, the authors present experiments in supervised and semi-supervised learning on point-cloud data as well as cosmology data.    The reviewers agreed that this is a very promising line of work and acknowledged the effort of the authors to improve their paper after the initial discussion phase. However, they also agree that the work appears to be missing more convincing numerical experiments and insights on the choice of neural architectures in the class of permutation-covariant.     In light of these reviews, the AC invites their work to the workshop track.   Also, I would like to emphasize an aspect of this work that I think should be addressed in the subsequent revision.    As the authors rightfully show (thm 2.1), permutation equivariance puts very strong constraints in the class of 1-layer networks. This theorem, while rigorous, reflects a simple algebraic property of matrices that commute with permutation matrices. It is therefore not very surprising, and the resulting architecture relatively obvious. So much so that it already exists in the literature. In fact, it is a particular instance of the graph neural network model of Scarselli et al. '09 (

A:
en
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