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].

RESUMEN:  El trabajo se presenta y describa la aplicación de una red social para noticias (4x4News), donde las noticias más relevantes estarán presentes (entre otras características). Esta aplicación se caracteriza por una pantalla divida en una matriz de 16x16, donde cada celda  es una noticia.   EVALUACIÓN GENERAL:  El trabajo está bien escrito y sigue un formato (casi) completamente adecuado para INFONOR. La aplicación suena interesante y potencialmente útil como un add-on para redes sociales ya comúnmente usadas (ej. Facebook).  Hay varios puntos independientes que considero que son importantes de resolver:  1) Si bien es un trabajo de ingeniería interesante, no puedo ver cuál es el aporte/contribución desde el punto científico ¿Qué se puede re-utilizar de este trabajo para un futuro trabajo científico?  2) Dado que ustedes ofrecen una aplicación, creo que es fundamental un link para (com)probar esta aplicación.  3) En mi opinión, el sitio Web [Link] se aproxima a lo propuesto por este trabajo. Estó es así, porque terra permite registro de usuarios (perfiles), comentar noticias, valorar noticias, y configurar el sitio Web para mostrar las noticias que un usuario desea.  4) Este podría ser un detalle menor, pienso que el uso de redes sociales se podría usar para algo más que solamente mostrar la noticia más importante (ej. mostrar una especie de tracking (social) de una(s) noticia(s) similar).  DETALLES MENORES:  - Al final de la introducción, el uso de BOLD no es recomendado. - “Los titulares no realizan un buen análisis”. ¿Qué es un “buen” análisis?. Durante el trabajo, se observa varios adjetivos usados de manera ambiguas y que son innecesarios (ej. “complejo proceso” en la sección 2.1). - Dentro de la sección 2: “Desarrollo”, se encuentra el trabajo relacionado (sección 2.1). Esta sección debería ser re-estructurada. - Las citas del tipo “X, y otros, AÑO” (ej. sección 2.2 “Wilson, y otros, 2014”), creo que están incorrectas. Deberían usar “et al”. - Sección 2.2, ¿la coincidencia de palabras es suficiente para decir que dos noticias son similares? Creo que un análsis más profundo es un necesario. - Explicar un poco más el concepto “bundles”. - En las referencias se usan números [1], pero en el texto no usa números. Recomiendo seguir el formato de INFONOR.
es

The paper describes an idea to learn phrasal representation and facilitate them in RNN-based language models and neural machine translation  -Strengths:  The  idea to incorporate phrasal information into the task is interesting.  - Weaknesses:  - The description is hard to follow. Proof-reading by an English native speaker would benefit the understanding - The evaluation of the approach has several weaknesses  - General discussion  - In Equation 1 and 2 the authors mention a phrase representation give a fix-length word embedding vector. But this is not used in the model. The representation is generated based on an RNN. What the propose of this description? - Why are you using GRU for the Pyramid and LSTM for the sequential part? Is the combination of two architectures a reason for your improvements? - What is the simplified version of the GRU? Why is it performing better? How is it performing on the large data set? - What is the difference between RNNsearch (groundhog) and RNNsearch(baseline) in Table 4? -  What is the motivation for only using the ending phrases and e.g. not using the starting phrases? - Did you use only the pyramid encoder? How is it performing? That would be a more fair comparison since it normally helps to make the model more complex. - Why did you run RNNsearch several times, but PBNMT only once?  - Section 5.2: What is the intent of this section
en

First, I'd like to thank the authors for their answers and clarifications. I find, the presentation of the multi-stage version of the model much clearer now.  Pros:  + The paper states a sparse coding problem using cosine loss, which allows to solve the problem in a single pass.  + The energy-based formulation allows bi-directional coding that incorporates top-down and bottom-up information in the feature extraction process.   Cons:  + The cost of running the evaluation could be large in the  multi-class setting, rendering the approach less attractive and the computational cost comparable to recurrent architectures.  + While the model is competitive and improves over the baseline, the paper would be more convincing with other comparisons (see text). The experimental evaluation is limited (a single database and a single baseline)  ------  The motivation of the sparse coding scheme is to perform inference in a feed forward manner. This property does not hold in the multi stage setting, thus optimization would be required (as clarified by the authors).  Having an efficient way of performing a bi-directional coding scheme is very interesting. As the authors clarified, this could not necessarily be the case, as the model needs to be evaluated many times for performing a classification.  Maybe an interesting combination would be to run the model without any class-specific bias, and evaluation only the top K predictions with the energy-based setting.  Having said this, it would be good to include a discussion (if not direct comparisons) of the trade-offs of using a model as the one proposed by Cao et al. Eg. computational costs, performance.  Using the bidirectional coding only on the top layers seems reasonable: one can get a good low level representation in a class agnostic way. This, however could be studied in more detail, for instance showing empirically the trade offs. If I understand correctly, now only one setting is being reported.  Finally, the authors mention that one benefit of using the architecture derived from the proposed coding method is the spherical normalization scheme, which can lead to smoother optimization dynamics. Does the baseline (or model) use batch-normalization? If not, seems relevant to test.   Minor comments:  I find figure 2 (d) confusing. I would not plot this setting as it does not lead to a function (as the authors state in the text).

en

