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].

Q: this proposes a multi-view learning approach for learning representations for acoustic sequences. they investigate the use of bidirectional LSTM with contrastive losses. experiments show improvement over the previous work.  although I have no expertise in speech processing, I am in favor of accepting this paper because of following contributions: - investigating the use of fairly known architecture on a new domain. - providing novel objectives specific to the domain - setting up new benchmarks designed for evaluating multi-view models  I hope authors open-source their implementation so that people can replicate results, compare their work, and improve on this work.

A: en
****
Q: This paper introduces a mechanism for active learning with convolutional neural networks (CNNs). I would not go as far as the authors in calling these "deep", seeing that they seem to have only 2 hidden layers with only 20 filters each. The active learning criterion is a greedy selection scheme based on variational free energy and a series of approximations.  The paper is sometimes hard to read, due to (a) many grammatical errors and (b) sloppy notation in some places (e.g., on page 5, line 1, f is used but never introduced before). Overall, I give an accepting score, but a weak one because of the grammatical errors. If the paper is accepted, these should be fixed for the final version, optimally by a native speaker.   The paper's topic is interesting, and the paper appears to succeed in its goal of showing a proof of concept for active learning in CNNs (if only on toy datasets). I'm surprised by the new results on uncertainty sampling and curriculum learning the authors added: why do these methods both break for USPS? In particular, uncertainty sampling did very well (in fact, better than the authors' new method) on MNIST, but apparently horribly on USPS; some explanation for this would be useful.  I have one more question: why is it necessary to first sample a larger subset D \subset U, from which we select using active learning? Is this merely done for reasons of computational efficiency, or can it actually somehow improve results? (If so, it would be instrumental to see the worse results when this is not done.)

A: en
****
Q: - Descripción de los componentes de la plataforma de cloud computing Windows Azure. - Sucinta descripción del concepto de cloud computing.   - Falta discusión respecto de las posibles desventajas o escenarios adversos de una "implementación en la nube" - El resumen habla de "...detallar la experiencia en la construcción de una plataforma para construir cloud públicas y abiertas", pero sólo se limita a la descripción de la plataforma Azure. - Poquísima profundidad en la descripción de la aplicación que se montó en la plataforma cloud. Dado el título y resumen, se espera más análisis en términos de las ventajas y desventajas del desarrollo usando esta plataforma. - Problemas de redacción al interior del mismo trabajo. - Ciertas secciones parecen más un "brochure" comercial que un trabajo académico.

A:
es
****