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].

Input: Consider Input: The paper describes a method to evaluate generative models such as VAE, GAN and GMMN. This is very much needed in our community where we still eyeball generated images to judge the quality of a model. However, the technical increment over the NIPS 16 paper: “Measuring the reliability of MCMC inference with bidirectional Monte Carlo” is very small, or nonexistent (but please correct me if I am wrong!).  (Grosse et al). The relative contribution of this paper is the application of this method to generative models.  In section 2.3 the authors seem to make a mistake. They write E[p’(x)] <= p(x) but I think they mean: E[log p’(x)] <= log E[p’(x)] = log p(x). Also,  for what value of x? If p(x) is normalized it can’t be true for all values of x. Anyways, I think there are typos here and there and the equations could be more precise. On page 5 top of the page it is said that the AIS procedure can be initialized with q(z|x) instead of p(z). However, it is unclear what value of x is then picked? Is it perhaps Ep(x)[q(z|x)] ? I am confused with the use of the term overfitting (p8 bottom). Does a model A overfit relative to a another model B if the test accuracy of A is higher than that of B even though the gap between train and test accuracy is also higher for B than for A. I think not. Perhaps the last sentence on page 8 should say that VAE-50 underfits less than GMMN-50? The experimental results are interesting in that it exposes the fact that GANs and GMMNs seem to have much lover test accuracy than VAE despite the fact that their samples look great.

Output: en


Input: Consider Input: De acuerdo con la convocatoria, Infonor-Chile acepta reportes de trabajos de investigación en temas de interés en computación y tecnologías de la información y sus aplicaciones; el artículo presenta una reflexión, desde el punto de vista del autor (en algunos casos sustentado en citas bibliográficas), sobre los desafíos de los Métodos Formales en el ámbito de la Ingeniería de Software, sin embargo no ofrece aportes sustentados en algún tipo de validación, más que la opinión del autor sobre las oportunidades y desafíos de dicha área; incluso la revisión bibliográfica no ofrece evidencia de haber utilizado un proceso formal (p.e. revisión sistemática) para sustentar la reflexión propuesta.  Me parece que como trabajo de "divulgación" es muy interesante, pero como reporte de "investigación" es limitado.

Output: es


Input: Consider Input: The authors apply the image captioning architecture of Xu et al. 2015 to video captioning. The model is extended to have attention over multiple layers of the ConvNet instead of just a single layer. Experiments on YouTube2Text, M-VAD and MSR-VTT show that this works better than only using one of the layers at a time.  I think this is solid work on the level of a well-executed course project or a workshop paper. The model makes sense, it is adequately described, and the experiments show that attending over multiple layers works better than attending over any one layer in isolation. Unfortunately, I don't think there is enough to get excited about here from a technical perspective and it's not clear what value the paper brings to the community. Other aspects of the paper, such as including the hard attention component, don't seem to add to the paper but take up space.   If the authors want to contribute a detailed, focused exploration of multi-level features this could become a more valuable paper, but in that case I would expect a much more thorough exploration of the choices and tradeoffs of different schemes without too many spurious aspects such as video features, hard attention, etc.


Output: en