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].
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Question: Comentarios del artículo. De forma: •	Problemas con el uso de mayúscula inicial. •	Desconformidad? Sería mejor inconformidad •	Problemas de ortografía. Faltan tildes en algunas palabras (por ejemplo: implantará), y sobran en otras (critérios). No tiene mucha presentación este tipo de errores teniendo en cuenta que los procesadores de texto cuentan con corrector de ortografía. •	Artículos en español deben escribirse siempre en tercera persona. •	Problemas con el sistema de referenciación.    De fondo: •	Las figuras 1 y 2 no aportan al entendimiento del problema y la solución. Favor insertar solo figuras que permitan una mejor comprensión de la necesidad. •	El orden para presentar la información es confuso. Primero debería abordarse la sección de teoría y de trabajos previos para tener el background necesario y así entender porque es necesaria la construcción de una nueva aplicación. •	En la introducción siempre el último párrafo debe dar cuenta de la estructura del artículo. Falta esto para que el lector tenga una visión general del paper. •	En la sección teoría del dominio mencionan la definición de CRM, pero al parecer lo que ofrecen como solución no es una aplicación de este tipo. Esto genera confusión en la lectura. Igualmente parece que están mezclando las definiciones con la sección donde comparan otras aplicaciones, o cual tampoco favorece el entendimiento del artículo. •	Al indicar las aplicaciones comparadas no queda muy claro qué criterios se usaron para su selección. Qué se revisó en cuanto a la literatura especializada? •	La calidad de la figura 3 no es adecuada para una publicación. •	La figura 4 y su etiqueta explicativa están en diferentes posiciones. Dicha figura también tiene problemas de calidad. •	La forma de mostrar la solución propuesta es muy confusa. Vale la pena reorganizar toda la sección para explicar primero el proceso propuesto en el desarrollo y posteriormente los resultados obtenidos. •	Teniendo en cuenta el objetivo del artículo, una sección de modelo de indicadores de la extensión usada le roba espacio a aspectos más importantes a tratar como por ejemplo la utilidad de que la aplicación sea móvil y cuente con datos como la ubicación del vendedor. •	El título del trabajo no se compadece del contenido. No se hizo énfasis en el componente móvil que aparentemente era la mayor bondad de la aplicación. •	La sección de estadísticas no conecta con el resto del contenido, y tampoco se usan leyendas explicativas para lograr esta conexión.

Answer: es


Question: Overall I think this is an interesting paper which shows empirical performance improvement over baselines. However, my main concern with the paper is regarding its technical depth, as the gist of the paper can be summarized as the following: instead of keeping the batch norm mean and bias estimation over the whole model, estimate them on a per-domain basis. I am not sure if this is novel, as this is a natural extension of the original batch normalization paper. Overall I think this paper is more fit as a short workshop presentation rather than a full conference paper.  Detailed comments:  Section 3.1: I respectfully disagree that the core idea of BN is to align the distribution of training data. It does this as a side effect, but the major purpose of BN is to properly control the scale of the gradient so we can train very deep models without the problem of vanishing gradients. It is plausible that intermediate features from different datasets naturally show as different groups in a t-SNE embedding. This is not the particular feature of batch normalization: visualizing a set of intermediate features with AlexNet and one gets the same results. So the premise in section 3.1 is not accurate.  Section 3.3: I have the same concern as the other reviewer. It seems to be quite detatched from the general idea of AdaBN. Equation 2 presents an obvious argument that the combined BN-fully_connected layer forms a linear transform, which is true in the original BN case and in this case as well. I do not think it adds much theoretical depth to the paper. (In general the novelty of this paper seems low)  Experiments:  - section 4.3.1 is not an accurate measure of the "effectiveness" of the proposed method, but a verification of a simple fact: previously, we normalize the source domain features into a Gaussian distribution. the proposed method is explicitly normalizing the target domain features into the same Gaussian distribution as well. So, it is obvious that the KL divergence between these two distributions are closer - in fact, one is *explicitly* making them close. However, this does not directly correlate to the effectiveness of the final classification performance.  - section 4.3.2: the sensitivity analysis is a very interesting read, as it suggests that only a very few number of images are needed to account for the domain shift in the AdaBN parameter estimation. This seems to suggest that a single "whitening" operation is already good enough to offset the domain bias (in both cases shown, a single batch is sufficient to recover about 80% of the performance gain, although I cannot get data for even smaller number of examples from the figure). It would thus be useful to have a comparison between these approaches, and also a detailed analysis of the effect from each layer of the model - the current analysis seems a bit thin.

Answer: en


Question: This paper proposes to use RNN and reinforcement learning for solving combinatorial optimization problems. The use of pointer network is interesting as it enables generalization to arbitrary input size. The proposed method also "fintunes" on test examples with active search to achieve better performance.  The proposed method is theoretically interesting as it shows that RNN and RL can be combined to solve combinatorial optimization problems and achieve comparable performance to traditional heuristic based algorithms.  However, the lack of complexity comparison against baselines make it impossible to tell whether the proposed method has any practical value. The matter is further complicated by the fact that the proposed method runs on GPU while baselines run on CPU: it is hard to even come up with a meaningful unit of complexity. Money spent on hardware and electricity per instance may be a viable option.  Further more, the performance comparisons should be taken with a grain of salt as traditional heuristic based algorithms can often give better performance if allowed more computation, which is not controlled across algorithms.

Answer:
en


