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].

Input: Consider Input: El artículo tiene dos contribuciones principales: una discusión sobre la utilidad de los controles SCADA sobre dispositivos móviles, una descripción de algunas de las aplicaciones concreta que implementan esta función en celulares o tablets.  Hay varios errores de escritura a lo largo del artículo. Por ejemplo se utilizan separadores al final de frase _, pero no en forma consistente. La primera y última oración del abstract parecen mal redactadas. En los párrafos finales de la introducción las listas no son claramente explicadas.  Si bien es relevante para la conferencia hacer una revisión de aplicaciones SCADA para móviles, no se define un mecanismo de búsqueda, ni un criterio para compararlas. ¿Cuáles son sus diferencias? ¿Cuál es adecuada en cada caso?  Se sugiere incluir una tabla comparativa de sus prestaciones, y más importante, definir criterios de comparación para poder aportar nuevos datos al lector. Las conclusiones retoman la introducción pero no responden qué aplicaciones convendría usar para el contexto de industria minera.

Output: es


Input: Consider Input: The paper describes an MT training data selection approach that scores and ranks general-domain sentences using a CNN classifier. Comparison to prior work using continuous or n-gram based language models is well done, even though  it is not clear of the paper also compared against bilingual data selection (e.g. sum of difference of cross-entropies). The motivation to use a CNN instead of an RNN/LSTM was first unclear to me, but it is a strength of the paper to argue that certain sections of a text/sentence are more important than others and this is achieved by a CNN. However, the paper does not experimentally show whether a BOW or SEQ (or the combination of both( representation is more important and why. The textual description of the CNN (one-hot or semi-supervised using pre-trained embeddings)  is clear, detailed, and points out the important aspects. However, a picture of the layers showing how inputs are combined would be worth a thousand words.  The paper is overall well written, but some parentheses for citations are not necessary (\citet vs. \citep) (e.g line 385).  Experiments and evaluation support the claims of the paper, but I am a little bit concerned about the method of determining the number of selected in-domain sentences (line 443) based on a separate validation set: - What validation data is used here? It is also not clear on what data hyperparameters of the CNN models are chosen. How sensitive are the models to this? - Table 2 should really compare scores of different approaches with the same number of sentences selected. As Figure 1 shows, the approach of the paper still seems to outperform the baselines in this case.   Other comments: - I would be interested in an experiment that compares the technique of the paper against baselines when more in-domain data is available, not just the development set. - The results or discussion section could feature some example sentences selected by the different methods to support the claims made in section 5.4. - In regards to the argument of abstracting away from surface forms in 5.4: Another baseline to compare against could have been the work of Axelrod, 2015, who replace some words with POS tags to reduce LM data sparsity to see whether the word2vec embeddings provide an additional advantage over this. - Using the sum of source and target classification scores is very similar to source & target Lewis-Moore LM data selection: sum of difference of cross-entropies. A reference to this work around line 435 would be reasonable.  Finally, I wonder if you could learn weights for the sum of both source & target classification scores by extending the CNN model to the bilingual/parallel setting.

Output: en


Input: Consider Input: The paper introduces a variation to the CNN-based texture synthesis procedure of Gatys et al. that matches correlations between spatially shifted feature responses in addition to the correlations between feature responses at the same position in the feature maps.  The paper claims that this  a) improves texture synthesis for textures with long-range regular structures, that are not preserved with the Gatys et al. method b) improves performance on texture inpainting tasks compared to the Gatys et al. method c) improves results in season transfer when combined with the style transfer method by Gatys et al.  Furthermore the paper shows that d) by matching correlations between spatially flipped feature maps, symmetry properties around the flipping axis can be preserved.  I agree with claim a). However, the generated textures still have some issues such as greyish regions so the problem is not solved. Additionally, the procedure proposed is very costly which makes an already slow texture synthesis method substantially slower. For example, in comparison, the concurrent work by Liu et al. (


Output: en