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 Input:
Trabajo muy mal presentado, con severos errores de ortografía y redacción. Pese al tema, interesante, no es posible encontrar un hilo adecuado en el desarrollo del trabajo.  Desde el inicio surgen los problemas, en el resumen se afirma que "el objetivo de este artículo es la realización de un análisis a un conjunto de registros de diagnósticos médicos, puestos a manera de investigación, en busca de una mejor perspectiva y visualización de los datos para responder a los indicadores que se plantearon" Primero, el objetivo del artículo no es la realización del análisis, sino mostrar el resultado de algún proceso (el proceso puede ser el análisis).La frase "puestos a manera de investigación" es del todo inadecuada.  No se responde a los indicadores..., ¿qué sentido tiene eso?  Las citas en itálico incluyen a los autores; faltó apoyo de alguien más experimentado en la escritura del artículo.  Lamentablemente, el trabajo está plagado de errores inaceptables.  No sólo de escritura.  La página 3 indica que uno de los problemas encontrados dice relación con la "Estructuración de datos". ¿Cuál es el problema? ¿Por qué se produce? ¿Cómo lo enfrenta el autor? ¿Es satisfactorio su enfoque?  Creo que esos antecedentes (que se repiten de manera similar en toda la extensión del texto), justifican mi rechazo.  Resumiendo, es muy pobre el trabajo.

Ex Output:
es


Ex Input:
This paper proposed a dynamic coattention network for the question answering task with long contextual documents.  The model is able to encode co-dependent representations of the question and the document, and a dynamic decoder iteratively pointing the potential answer spans to locate the final answer.   Overall, this is a well-written paper.  Although the model is a bit complicated (coattention encoder, iterative dynamic pointering decoder and highway maxout network), the intuitions behind and the details of the model are clearly presented.  Also the performance on the SQuAD dataset is good.  I would recommend this paper to be accepted.

Ex Output:
en


Ex Input:
Variational auto-encoders, adversarial networks, and kernel scoring rules like MMD have recently gained popularity as methods for learning directed generative models and for other applications like domain adaptation. This paper gives an additional method along the scoring rules direction that uses the matching of central moments to match two probability distributions. The technique is simple, and in the case of domain adaptation, highly effective.  CMD seems like a very nice and straightforward solution to the domain adaptation problem. The method is computationally straightforward to implement, and seems quite stable with respect to the tuning parameters when compared to MMD. I was skeptical reading through this, especially given the fact that you only use K=5 in your experiments, but the results seem quite good. The natural question that I have now is: how will this method do in training generative models? This is beyond the scope of this paper, but it’s the lowest hanging fruit.  Below I give more detailed feedback.  One way to speed up MMD is to use a random Fourier basis as was done in “Fastmmd: Ensemble of circular discrepancy for efficient two-sample test” by Zhao and Meng, 2015. There are also linear time estimators, e.g., in “A Kernel Two-Sample Test“ by Gretton et al., 2012. I don’t think you need to compare against these approaches since you compare to the full MMD, but they should be cited.  The paper “Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy” by Sutherland et al. submitted to ICLR 2017 as well, discusses techniques for optimizing the kernel used in MMD and is worth citing in section 3.  How limiting is the assumption that the distribution has independent marginals?  The sample complexity of MMD depends heavily on the dimensionality of the input space - do you have any intuitions about the sample complexity of CMD? It seems like it's relatively insensitive based on the results in Figure 4, but I would be surprised if this were the case with 10,000 hidden units. I mainly ask this because with generative models, the output space can be quite high-dimensional.  I’m concerned that the central moments won’t be numerically stable at higher orders when backpropagating. This doesn’t seem to be a problem in the experimental results, but perhaps the authors could comment a bit on this? I’m referring to the fact that ck(X) can be very large for k >= 3. Proposition 1 alleviates my concerns that the overall objective is unstable, I’m referring specifically to the individual terms within.  Figure 3 is rather cluttered, and aside from the mouse class it’s not clear to me from the visualization that the CMD regularizer is actually helping. It would be useful to remove some of the classes for the purpose of visualization.  I would like some clarification about the natural geometric interpretations of K=5. Do you mean that the moments up to K=5 have been well-studied? Do you have any references for this? Why does K >= 6 not have a natural geometric interpretation?  Figure 4 should have a legend

Ex Output:

en