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]: El artículo realiza un recorrido por definiciones de calidad, calidad de software y modelos de calidad. Algunos modelos se tratan primero en una sección preliminar y otros luego del análisis teórico, sugiero re-organizar la presentación de conceptos. En mi opinión, los conceptos de modelo de calidad y modelo conceptual no son equivalentes, aunque se tratan de forma conjunta en la introducción.  En la segunda sección se presentan varios modelos, pero no se establece claramente las relaciones entre ellos y cuál es el criterio para tenerlos en cuenta en esta línea de investigación. Existen múltiples propuestas de modelos de calidad de software y modelos específicos para web. Por otro lado, las guías de OWASP son relevantes para la seguridad de aplicaciones web, no son un modelo de calidad.  Sugiero exponer el análisis teórico (que incluye definiciones básicas de calidad) antes de la descripción y justificación de los modelos considerados. Por otro lado, se podrían agregar conceptos / atributos / modelos de la calidad específicos en el contexto de los sistemas bancarios.  En mi opinión, dedicar tanto espacio a definir el concepto de banca electrónica,  banca por internet y luego el de banca móvil, no resulta necesario en el estado del arte actual y el conocimiento público de estos conceptos. Sugiero centrarse en los objetivos de investigación de este trabajo en particular.  Las descripciones del marco metodológico DESMET y la metodología investigación – acción son muy breves. Es necesario establecer antes los objetivos de la investigación.  Los modelos conceptuales obtenidos son el resultado principal del trabajo. Se deben utilizar notaciones más legibles (sugiero también poner información extendida como anexo del artículo o en la web). Se debería explicar además como estos modelos fueron validados, para que sean confiables si son utilizados para definir ontologías o usados en trabajos posteriores.
[A]: es


[Q]: EDIT: Updated score. See additional comment.  I quite like the main idea of the paper, which is based on the observation in Sec. 3.0 - that the authors find many predictable patterns in the independent evolution of weights during neural network training. It is very encouraging that a simple neural network can be used to speed up training by directly predicting weights.  However the technical quality of the current paper leaves much to be desired, and I encourage the authors to do more rigorous analysis of the approach. Here are some concrete suggestions:  - The findings in Section 3.0 which motivate the approach, should be clearly presented in the paper. Presently they are stated as anecdotes.  - A central issue with the paper is that the training of the Introspection network I is completely glossed over. How well did the training work, in terms of training, validation/test losses? How well does it need to work in order to be useful for speeding up training? These are important questions for anyone interested in this approach.  - An additional important issue is that of baselines. Would a simple linear/quadratic model also work instead of a neural network? What about a simple heuristic rule to increase/decrease weights? I think it's important to compare to such baselines to understand the complexity of the weight evolution learned by the neural network.  - I do not think that default tensorflow example hyperparameters should be used, as mentioned by authors on OpenReview. There is no scientific basis for using them. Instead, first hyperparameters which produce good results in a reasonable time should be selected as the baseline, and then added the benefit of the introspection network to speed up training (and reaching a similar result) should be shown.  - The authors state in the discussion on OpenReview that they also tried RNNs as the introspection network but it didn't work with small state size. What does "didn't work" mean in this context? Did it underfit? I find it hard to imagine that a large state size would be required for this task. Even if it is, that doesn't rule out evaluation due to memory issues because the RNN can be run on the weights in 'mini-batch' mode. In general, I think other baselines are more important than RNN.  - A question about jump points:  The I is trained on SGD trajectories. While using I to speed up training at several jump points, if the input weights cross previous jump points, then I gets input data from a weight evolution which is not from SGD (it has been altered by I). This seems problematic but doesn't seem to affect your experiments. I feel that this again highlights the importance of the baselines. Perhaps I is doing something extremely simple that is not affected by this issue.  Since the main idea is very interesting, I will be happy to update my score if the above concerns are addressed.
[A]: en


[Q]: - Strengths: Nicely written and understandable. Clearly organized. Targeted answering of research questions, based on  different experiments.  - Weaknesses: Minimal novelty. The "first sentence" heuristic has been in the summarization literature for many years. This work essentially applies this heuristic (evolved) in the keyword extraction setting. This is NOT to say that the work is trivial: it is just not really novel.  Lack of state-of-the-art/very recent methods. The experiment on the system evaluation vs state-of-the-art systems simply uses strong baselines. Even though the experiment answers the question "does it perform better than baselines?", I am not confident it illustrates that the system performs better than the current state-of-the-art. This somewhat reduces the value of the paper.  - General Discussion: Overall the paper is good and I propose that it be published and presented.   On the other hand, I would propose that the authors position themselves (and the system performance) with respect to: Martinez‐Romo, Juan, Lourdes Araujo, and Andres Duque Fernandez. "SemGraph: Extracting keyphrases following a novel semantic graph‐based approach." Journal of the Association for Information Science and Technology 67.1 (2016): 71-82. (with which the work holds remarkable resemblance in some points)  Le, Tho Thi Ngoc, Minh Le Nguyen, and Akira Shimazu. "Unsupervised Keyphrase Extraction: Introducing New Kinds of Words to Keyphrases." Australasian Joint Conference on Artificial Intelligence. Springer International Publishing, 2016.
[A]:
en