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:
Los datos abiertos de Chile Este trabajo analiza de manera general un tema que ha adquirido real relevancia en los gobiernos a nivel mundial, los datos abiertos.  La problemática que aborda el artículo está sustentada adecuadamente en las necesidades que existen de proveer datos para evaluar la transparencia y el interés público. Se realiza una investigación aplicada a las principales plataformas web gubernamentales  en Chile. El objetivo está declarado por el autor  y establece que se  presenta el estado de los datos abiertos en Chile y se realiza un diagnóstico  de los principales sitios y herramientas provistas por el estado para mejorar la transparencia y potenciar la participación  ciudadana. La revisión bibliográfica es relevante al tema pero bastante general. El modelo que se utiliza para medir la calidad de los datos en la web  es elegido sin justificar a través de  un análisis que establezca su eficiencia en comparación a otros modelos.  La revisión que se realiza del concepto de datos abiertos es mínima y no está  presente en la revisión realizada como este tema ha sido abordado en diferentes países y  los resultados que han obtenido. Los autores deberían  profundizar más el concepto de datos abiertos a nivel gubernamental para transparencia y  el interés público y sustentar mejor esta división en la literatura. El aspecto metodológico del trabajo se  enfoca a utilizar el modelo de 5 estrellas y describir una clasificación general que se realiza en base a este modelo. Aunque este enfoque metodológico es adecuado y se asocia claramente a la temática no presenta proposiciones a evaluar (preguntas de investigación o hipótesis a testear). El diagnóstico es básico y general a partir de un cuatro variables que permiten clasificar a cada plataforma. El autor  debería establecer también una categorización de lo que son los  sujetos de estudio (se menciona al  de gobierno de Chile pero son solo cuatro plataformas del total de plataformas que posee el gobierno). En este sentido debería referirse a plataformas transversales de  servicio centrales (ya que no analiza gobiernos locales en ningún momento) Este trabajo  se sustenta en la utilización de un modelo de diagnóstico ya validado en la literatura  sin embargo su aplicación es limitada y a mi juicio no permite generalizar  para el gobierno en su totalidad.  La contribución del trabajo es importante ya que presenta información relevante en el proceso de llegar a lo que es el gobierno abierto. Sin embargo la generalización de sus resultados es cuestionable ya que  está limitada  a  cuatro variables y cuatro sitios web. En  las conclusiones no se reflejan  con toda claridad las principales  contribuciones del articulo y se sugiere a los autores mejorarlas  incluyendo aspectos relevantes a la temática evaluada (los sitios web), los aportes  al estudio del gobierno abierto en Chile y reconocer las limitaciones que tiene este estudio. Los aspectos formales del trabajo son satisfactorios para el congreso.

Ex Output:
es


Ex Input:
In this interesting paper the authors explore the idea of using an ensemble of multiple discriminators in generative adversarial network training. This comes with a number of benefits, mainly being able to use less powerful discriminators which may provide better training signal to the generator early on in training when strong discriminators might overpower the generator.  My main comment is about the way the paper is presented. The caption of Figure 1. and Section 3.1 suggests using the best discriminator by taking the maximum over the performance of individual ensemble members. This does not appear to be the best thing to do because we are just bound to get a training signal that is stricter than any of the individual members of the ensemble. Then the rest of the paper explores relaxing the maximum and considers various averaging techniques to obtain a ’soft-discriminator’. To me, this idea is far more appealing, and the results seem to support this, too. Skimming the paper it seems as if the authors mainly advocated always using the strongest discriminator, evidenced by my premature pre-review question earlier.  Overall, I think this paper is a valuable contribution, and I think the idea of multiple discriminators is an interesting direction to pursue.

Ex Output:
en


Ex Input:
This paper proposes a design principle for computation blocks in convolutional networks based on repeated application of expand and join operations resulting in a fractal-like structure.   This paper is primarily about experimental evaluation, since the objective is to show that a residual formulation is not necessary to obtain good performance, at least on some tasks.  However, in my opinion the evaluations in the paper are not convincing. The primary issue is lack of a proper baseline, against which the improvements can be clearly demonstrated by making isolated changes. I understand that for this paper such a baseline is hard to construct, since it is about a novel architecture principle. This is why more effort should be put into this, so that core insights from this paper can be useful even after better performing architectures are discovered. The number of parameters and amount of computation should be used to indicate how fair the comparisons are between architectures. Some detailed comments:  - In Table 1 comparisons to Resnets, the resnets from He et al. 2016b and Wide Resnets should be compared to FractalNet (in lieu of a proper baseline). The first outperforms FractalNet on CIFAR-100 while the second outperforms it on both. The authors compare to other results without augmentation, but did not perform additional experiments without augmentation for these architectures.  - The 40 layer Fractal Net should not be compared to other models unless the parameter reduction tricks are utilized for the other models as well.  - A proper comparison to Inception networks should also be performed for these networks. My guess is that the reason behind a seemingly 'ad-hoc' design of Inception modules is to reduce the computational footprint of the model (which is not a central motivation of fractal nets). Since this model is directly related to the Inception module due to use of shorter and longer paths without shortcuts, one can easily simplify the Inception design to build a strong baseline e.g. by converting the concatenation operation to a mean operation among equally sized convolution outputs. As an aside, note that Inception networks have already shown that residual networks are not necessary to obtain the best performance [1].  - It should be noted that Residual/Highway architectures do have a type of anytime property, as shown by lesioning experiments in Srivastava et al and Viet et al.  - The architecture specific drop-path regularization is interesting, but is used along with other regularizers such as dropout, batch norm and weight decay and its benefit on its own is not clear.  Overall, it's not clear to me that the experiments clearly demonstrate the utility of the proposed architecture.   [1] Szegedy, Christian, Sergey Ioffe, and Vincent Vanhoucke. "Inception-v4, inception-resnet and the impact of residual connections on learning." arXiv preprint arXiv:1602.07261 (2016).

Ex Output:

en