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

[Q]: Muchos son los estudios sobre el efecto de las redes sociales, las oportunidades que pueden generar y de influencia en el comportamiento del consumidor. Quizas la mas grande contribucion de las redes sociales sean recriar la realidad en un Universo que puede ser mejor observable. Sin embargo mucho antes de las redes sociales, de la Internet y de los ultimos avances tecnologicos, el comportamiento del consumidor ya era observado e investigado por diferentes areas del conocimiento. Asi, estudiar el marketing boca a boca, uno de los registros mas antiguos y fiables aplicados a las redes sociales es de gran importancia. Entender las redes sociales y su multiple variedad es posibilitar ampliar los estudios a traves de otras dimensiones. El uso de las correlaciones y el modelo adoptado abre la posibilidad de su uso en otras realidades, otros paises. Por estos motivos me parece conveniente recomendar el articulo segun el criterio de "fuertemente aceptado" al cumplir con el rigor metodologico, ampliar las cuestiones, probar un modelo y despertar interes para otros investigadores.
[A]: es

[Q]: This work proposes a convolutional architecture for any graph-like input data (where the structure is example-dependent), or more generally, any data where the input dimensions that are related by a similarity matrix. If instead each input example is associated with a transition matrix, then a random walk algorithm is used generate a similarity matrix. Developing convolutional or recurrent architectures for graph-like data is an important problem because we would like to develop neural networks that can handle inputs such as molecule structures or social networks. However, I don't think this work contributes anything significant to the work that has already been done in this area. The two main proposals I see in this paper are: 1) For data associated with a transition matrix, this paper proposes that the transition matrix be converted to a similarity matrix. This seems obvious. 2) For data associated with a similarity matrix, the k nearest neighbors of each node are computed and supply the context information for that node. This also seems obvious. Perhaps I have misunderstood the contribution, but the presentation also lacks clarity, and I cannot recommend this paper for publication. Specific Comments: 1) On page 4: "An interesting attribute of this convolution, as compared to other convolutions on graphs is that, it preserves locality while still being applicable over different graphs with different structures." This is false; the other proposed architectures can be applied to inputs with different structures (e.g. Duvenaud et. al., Lusci et. al. for NN architectures on molecules specifically).
[A]:
en