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]: 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.
[A]: es

[Q]: The paper is clearly written, and the claims are well-supported. The Related Work in particular is very thorough, and clearly establishes where the proposed work fits in the field. I had two main questions about the method: (1) phrases are mentioned in section 3.1, but only word representations are discussed. How are phrase representations derived? (2) There is no explicit connection between M^+ and M^- in the model, but they are indirectly connected through the tanh scoring function. How do the learned matrices compare to one another (e.g., is M^- like -1*M^+?)? Furthermore, what would be the benefits/drawbacks of linking the two together directly, by enforcing some measure of dissimilarity? Additionally, statistical significance of the observed improvements would be valuable. Typographical comments: - Line 220: "word/phase pair" should be "word/phrase pair" - Line 245: I propose an alternate wording: instead of "entities are translated to," say "entities are mapped to". At first, I read that as a translation operation in the vector space, which I think isn't exactly what's being described. - Line 587: "slightly improvement in F-measure" should be "slight improvement in F-measure" - Line 636: extraneous commas in citation - Line 646: "The most case" should be "The most likely case" (I'm guessing) - Line 727: extraneous period and comma in citation
[A]: en

[Q]: This paper is technically sound. It highlights well the strengths and weaknesses of the proposed simplified model. In terms of impact, its novelty is limited, in the sense that the authors did seemingly the right thing and obtained the expected outcomes. The idea of modeling deep learning computation is not in itself particularly novel. As a companion paper to an open source release of the model, it would meet my bar of acceptance in the same vein as a paper describing a novel dataset, which might not provide groundbreaking insights, yet be generally useful to the community. In the absence of released code, even if the authors promise to release it soon, I am more ambivalent, since that's where all the value lies. It would also be a different story if the authors had been able to use this framework to make novel architectural decisions that improved training scalability in some way, and incorporated such new insights in the paper. UPDATED: code is now available. Revised review accordingly.
[A]:
en