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].

In this paper, the authors propose a Bayesian variant of the skipgram model to learn word embeddings. There are two important variant compared to the original model. First, aligned sentences from multiple languages are used to train the model. Therefore, the context words of a given target word can be either from the same sentence, or from an aligned sentence in a different language. This allows to learn multilingual embedding. The second difference is that each word is represented by multiple vectors, one for each of its different senses. A latent variable z models which sense should be used, given the context.  Overall, I believe that the idea of using a probabilistic model to capture polysemy is an interesting idea. The model introduced in this paper is a nice generalization of the skipgram model in that direction. However, I found the paper a bit hard to follow. The formulation might probably be simplified (e.g. why not consider a target word w and a context c, where c is either in the source or target language. Since all factors are independent, this should not change the model much, and would make the presentation easier). The performance of all models reported in Table 2 & 3 seem pretty low.  Overall, I like the main idea of the paper, which is to represent word senses by latent variables in a probabilistic model. I feel that the method could be presented more clearly, which would make the paper much stronger. I also have some concerns regarding the experimental results.  Pros: Interesting extension of skipgram to capture polysemy. Cons: The paper is not clearly written. Results reported in the paper seems pretty low.
en

es interesante constatar la preocupación de personas individuales que se preocupen de la seguridad y el mejor servicio que pueda brindar la empresa.   La figura 1 no es auto explicativa en términos de explicar los componentes del sistema, por ejemplo, TVL, que está a bordo de las locomotoras. Los Acrónimos definidos no presentan o representan su origen o fuente. Por ejemplo, VHF, SMS, XML, GPS, PLC, los que carecen de significado. Se dice que la información está disponible en diagramas y herramientas como Google Earth, cómo así.? Acaso, permite visualizar imágenes en 3D, combina imágenes de satélite, mapas y el motor de búsqueda de Google, permitiendo ver imágenes a escala de un lugar específico de la ruta.?.es eso lo que se logró?. Existen una serie de conclusiones, las cuales no son documentadas. Se dice, presentó un alza de 6%, sobre cuánto?. ¿Cómo son o cómo se obtienen los indicadores de eficiencia.? Bibliografía, mal citada.
es

The authors proposed to use leaky rectified linear units replacing binary units in Gaussian RBM.  A sampling method was presented to train the leaky-ReLU RBM. In the experimental section, AIS estimated likelihood on Cifar10 and SVHN were reported.   It's interesting for trying different nonlinear hidden units for RBM. However, there are some concerns for the current work.  1. The author did not explain why the proposed sampling method (Alg. 2) is correct. And the additional computation cost (the inner loop and the projection) should be discussed.  2. The results (both the resulting likelihood and the generative samples) of Gaussian RBM are much worse than what we have experienced. It seems that the Gaussian RBM were not trained properly.  3. The representation learned from a good generative model often helps the classification task when there are fewer label samples. Gaussian RBM works well for texture synthesis tasks in which mixing is an important issue. The authors are encouraged to do more experiments in these two direction.

en

