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: Este articulo presenta un Analisis Comparativo de Modelos de Madurez en Business Intelligence, mostrando las tecnicas DEA y MESME para realizar este analisis. Aun cuando el metodo en si queda claro, no se entiende si el modelo resultante queda seleccionado para cualquier organizacion, es decir, si es general. Deberia utilizar Inteligencia de negocios, en vez de Business Intelligence. Estos modelos seguramente estan definidos para grandes empresas de Estados Unidos, por lo tanto funcionan igualmente en las empresas chilenas? La madurez en Inteligencia de Negocios en empresas de paises desarrollados es distinta a paises como Chile. No es necesario incluir el punto II por si solo. En el punto V, parrafo 2, indica que se describe una adaptacion de DEA y no especifica la referencia o si es una adaptacion propuesta por los autores, ademas de indicar por que se utiliza una adaptacion. En la pagina 4, primer parrafo indica que se excluye el modelo Hierarchy y luego lo vuelve a incorporar en los pasos siguientes. En la pagina 5, letra E, no queda claro el fundamento de por que para aumentar la eficiencia se debe aumentar en un 10% los output. En la letra F debe cambiar la palabra 'podemos' y en el segundo parrafo parece que la tabla a referenciar es la 5 y no la 6. Debe describir los elementos de la ecuacion (3). En la letra B, de VI, no se entiende por que se selecciono el modelo EI, si tenia menor eficiencia. En la figura 6 no quedan claros los graficos de cada modelo. En los puntos V y VI los pasos los etiqueta con numeros y luego los describe con letras. Debe utilizar un editor de ecuaciones. El articulo no tiene el formato especificado para esto y tiene mas de 8 paginas. En la pagina 7 al incluir la tabla produce un desajuste en el texto que debe arreglar.
Example Output: es

Example Input: This work presents a novel 3D CNN architecture for climate event detection that combines an unsupervised auto-encoder reconstruction loss with YOLO like bounding box prediction. The approach is trained and evaluated on a large-scale, simulated climate dataset labeled by a costly heuristic approach called TECA. For the most part, the paper is nicely written (minor comments below) and addresses an important and well motivated problem. The authors provide sufficient model details to allow reproduction (although public code would be preferred). I find the experiments a bit unconvincing (see below) but appreciate the attention to model capacity (via number of parameter) when comparing the 2D and 3D model variants. I am concerned that the evaluation may be insufficient to assess the effectiveness of this method. An IoU threshold of 0.1 allows for many rather poor detections to count as true positives. If the goal of this work is to count the number of such instances this is defensible, but for localization this seems overly loose.Furthermore, the 3D CNN architecture (which is one of the the core novelties of this work compared to past efforts) does not seem capable of producing variable sized boxes (as noted in the last paragraph of page 7), which I imagine results in poorer performance at higher IoU thresholds (as many of the weather events appear small). The experiments also feel inconclusive about the effect of temporal modeling and semi-supervision. The temporal component does not seem to matter in the supervised settings (2D 51.45 mAP - 3D 51.00 mAP) but improves somewhat in the semi-supervised case (2D 51.11 mAP - 3D 52.92 mAP). Whereas the additional unlabeled data seems to hurt in the 2D case but improve results for the 3D model. Could the authors provide confidence intervals for these numbers? I would like to see further discussion of these trends especially with respect to the effect of the loss weights (alpha, beta, and gamma). I also note that it is not clear if both the 2D and 3D models were trained for equivalent time periods (seems like no from last paragraph of page 7). Could a plot of training and validation accuracy for each model be presented for comparison? Finally, is there any baseline approach the authors could report or compare too? Without one, it is difficult to evaluate the performance of the approach with respect to the difficulty of the problem. Preliminary Rating: I think this is an interesting paper that is well motivated but feel the experiments as presented do not seem adequate to support any conclusive trends. I would like to see the mAP trends across a wider range of IoU values and further discussion of training procedure, loss weight settings, and reasons for lack of bounding box variability in the 3D model (as stated above). Clarification: In the paper you say "While climate models are run on a 3D grid, with the vertical dimension corresponding to 30 levels; we only consider surface quantities (i.e. 2D data) in this study." Could you elaborate on what the surface quantities correspond to? Is it the highest cloud level? Minor notes: Please provide years for Prabhat et al. references rather than a and b. Footnote in 4.2 could be inline text with similar space. 4.3 second paragraph the word table is not capitalized like elsewhere. 4.3 4th paragraph the word section is not capitalized like elsewhere. Edit: I appreciate the authors responding to my questions but still feel the relatively poor localization performance at stricter IoU thresholds fails to justify the complexity of the approach. I encourage the authors to continue pursuing this line of research.
Example Output: en

Example Input: In this paper a well known soft mixture of experts model is adapted for, and applied to, a specific type of transfer learning problem in reinforcement learning (RL), namely transfer of action policies and value functions between similar tasks. Although not treated as such, the experimental setup is reminiscent of hierarchical RL works, an aspect which the paper does not consider at length, regrettably. One possible implication of this work is that architecture and even learning algorithm choices could simply be stated in terms of the objective of the target task, rather than being hand-engineered by the experimenter. This is clearly an interesting direction of future work which the paper illuminates. Pros: The paper diligently explains how the network architecture fits in with various widely used reinforcement learning setups, which does facilitate continuation of this work. The experiments are good proofs of concept, but do not go beyond that i.m.h.o. Even so, this work provides convincing clues that collections of deep networks, which were trained on not entirely different tasks, generalize better to related tasks when used together rather than through conventional transfer learning (e.g. fine-tuning). Cons: As the paper well recounts in the related work section, libraries of fixed policies have long been formally proposed for reuse while learning similar tasks. Indeed, it is well understood in hierarchical RL literature that it can be beneficial to reuse libraries of fixed (Fernandez & Veloso 2006) or jointly learned policies which may not apply to the entire state space, e.g. options (Pricop et. al). What is not well understood is how to build such libraries, and this paper does not convincingly shed light in that direction, as far as I can tell. The transfer tasks have been picked to effectively illustrate the potential of the proposed architecture, but the paper does not tackle negative transfer or compositional reuse in well known challenging situations outlined in previous work (e.g. Parisotto et. al 2015, Rusu el. al 2015, 2016). Since the main contributions are of an empirical nature, I am curious how the results shown in figures 6 & 7 look plotted against wall-clock time, since relatively low data efficiency is not a limitation for achieving perfect play in Pong (see Mnih. et al, 2015). It would be more illuminating to consider tasks where final performance is plausibly limited by data availability. It would also be interesting if the presented results were achieved with reduced amounts of computation, or reduced representation sizes compared to learning from scratch, especially when one of the useful source tasks is an actual policy trained on the target task. Finally, it is perhaps underwhelming that it takes a quarter of the data required for learning Pong from scratch just to figure out that a perfect Pong policy is already in the expert library. Simply evaluating each expert for 10 episodes and using an average-score-weighted majority vote to mix action choices would probably achieve the same final performance for a smaller fraction of the data.
Example Output:
en