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 Q]: La verificacion de la arquitectura de un producto software puede ser una gran contribucion a la calidad del mismo, por lo cual, cualquier herramienta que facilite esta labor es un aporte a la ingenieria de software. Presentan evidencias de una revision previa de metodos y herramientas alternativas o complementarias a la propuesta. Las descripciones presentadas se acompanan de ejemplos aclaratorios. No queda claro el rol e interaccion del experto del dominio. En las secciones iniciales dice que la herramienta realiza analisis estatico y dinamico, sin embargo al final del articulo, en la identificacion de posibles mejoras de SAVE se propone incluir el analisis dinamico, lo que se contradice con la afirmacion inicial. La descripcion del funcionamiento de la herramienta no esta lo suficientemente claro. La calidad de la figuras no es adecuada, por el tamano y resolucion de las mismas no es posible apreciar bien los detalles. La redaccion de articulo puede ser mejorada. Hay varias frases en donde faltan conectores, por ejemplo "En el presente apartado, veremos S.A.V.E. logra recuperar las dependencias existentes en el codigo fuente...." Muchos errores en la acentuacion de las palabras.
[EX A]: es

[EX Q]: This paper proposes a new kind of expert model where a sparse subset of most reliable experts is chosen instead of the usual logarithmic opinion pool of a PoE. I find the paper very unclear. I tried to find a proper definition of the joint model p(x,z) but could not extract this from the text. The proposed "EM-like" algorithm should then also follow directly from this definition. At this point I do not see if such as definition even exists. In other words, is there is an objective function on which the iterates of the proposed algorithm are guaranteed to improve on the train data? We also note that the "product of unifac models" from Hinton tries to do something very similar where only a subset of the experts will get activated to generate the input:
[EX A]: en

[EX Q]: This paper considers the problem of model-based policy search. The authors consider the use of Bayesian Neural Networks to learn a model of the environment and advocate for the $lpha$-divergence minimization rather than the more usual variational Bayes. The ability of alpha-divergence to capture bi-modality however comes at a price and most of the paper is devoted to finding tractable approximations. The authors therefore use the approach of Hernandez-Lobato et al. (2016) as proxy to the alpha-divergence . The environment/system dynamics is clearly defined as a well as the policy parametrization (section 3) and would constitute a useful reference point for other researchers. Simulated roll-outs, using the learned model, then provide samples of the expected return. Since a model of the environment is available, stochastic gradient descent can be performed in the usual way, without policy gradient estimators, via automatic differentiation tools. The experiments demonstrate that alpha-divergence is capable of capturing multi-model structure which competing methods (variational Bayes and GP) would otherwise struggle with. The proposed approach also compares favorably in a real-world batch setting. The paper is well-written, technically rich and combines many recent tools into a coherent algorithm. However, the repeated use of approximations to original quantities seems to somehow defeat the benefits of the original problem formulation. The scalability and computational effectiveness of this approach is also questionable and I am uncertain if many problem would warrant such complexity in their solution. As with other Bayesian methods, the proposed approach would probably shine in low-samples regime and in this case might be preferable to other methods in the same class (VB, GP).
[EX A]:
en