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].

Example Input: The paper proposed to analyze several recently developed machine readers and found that some machine readers could potentially take advantages of the entity marker (given that the same marker points out to the same entity). I usually like analysis papers, but I found the argument proposed in this paper not very clear.  I like the experiments on the Stanford reader, which shows that the entity marker in fact helps the Stanford reader on WDW. I found that results rather interesting.  However, I found the organization and the overall message of this paper quite confusing. First of all, it feels that the authors want to explain the above behavior with some definition of the “structures”. However, I am not sure that how successful the attempt is. For me, it is still not clear what the structures are. This makes reading section 4 a bit frustrating.   I am also not sure what is the take home message of this paper. Does it mean that the entity marking should be used in the MR models? Should we design models that can also model the entity reference at the same time? What are the roles of the linguistic features here? Should we use linguistic structure to overcome the reference issue?  Overall, I feel that the analysis is interesting, but I feel that the paper can benefit from having a more focused argument.
Example Output: en

Example Input: El artículo muestra un estudio de aplicación de Algoritmos Genéticos para resolver un sistema de ecuaciones en imágenes ópticas de detección de tumores y seguimiento de tratamientos oncológicos.  El trabajo está bien escrito y tiene un alto nivel de detalle técnico. Sin embargo, no muestra explícitamente cuáles son sus principales contribuciones. En este sentido sería conveniente ampliar la Sección de Introducción para entregar al lector un detalle de las principales contribuciones del trabajo y además incluir en esta sección (o en una sección de Trabajo Relacionados o Estado del Arte) un mayor detalle de otros trabajos previos que estén relacionados con la propuesta de los autores.  En el ámbito de aplicación de Algoritmos Genéticos para resolución de sistemas de ecuaciones, este trabajo no presenta un aporte novedoso, ya que existen varios trabajos previos orientados a este tipo de solución. Respecto al análisis de imágenes y reconocimiento de patrones, hay trabajos previos que ya utilizan Algoritmos Genéticos para este tipo de propuestas, tales como Dourdan, 2015.  Sería conveniente haber tenido una comparativa con otros métodos de Machine Learning que se han aplicado ampliamente al reconocimiento de imágenes tales como k-means o PCA.  Menores: - En la figura 10 no se visualiza bien la serie Solución AG por la cantidad de puntos graficados. - Último párrafo de Conclusiones. " ... la ventaja de trabajar con heurísticas como el algoritmo genéticos..." debiera ser "... la ventaja de trabajar con heurísticas como los algoritmos genéticos...". - Último párrafo de Conclusiones. "... es factible que el AG de buena aproximación..." creo que se debe cambiar la redacción, no se entiende en la primera lectura. -- Referencias. Las referencias a trabajos de Pattern Recognition es antigua 1997, creo que hay referencias más actualizadas y hay mayor cantidad de trabajos donde se aplican diversas técnicas de machine learning para reconocimiento de patrones, desde supervised a unsupervised métodos.
Example Output: es

Example Input: 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.
Example Output:
en