instruction:
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].
question:
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.
answer:
en


question:
This paper proposes a design principle for computation blocks in convolutional networks based on repeated application of expand and join operations resulting in a fractal-like structure.   This paper is primarily about experimental evaluation, since the objective is to show that a residual formulation is not necessary to obtain good performance, at least on some tasks.  However, in my opinion the evaluations in the paper are not convincing. The primary issue is lack of a proper baseline, against which the improvements can be clearly demonstrated by making isolated changes. I understand that for this paper such a baseline is hard to construct, since it is about a novel architecture principle. This is why more effort should be put into this, so that core insights from this paper can be useful even after better performing architectures are discovered. The number of parameters and amount of computation should be used to indicate how fair the comparisons are between architectures. Some detailed comments:  - In Table 1 comparisons to Resnets, the resnets from He et al. 2016b and Wide Resnets should be compared to FractalNet (in lieu of a proper baseline). The first outperforms FractalNet on CIFAR-100 while the second outperforms it on both. The authors compare to other results without augmentation, but did not perform additional experiments without augmentation for these architectures.  - The 40 layer Fractal Net should not be compared to other models unless the parameter reduction tricks are utilized for the other models as well.  - A proper comparison to Inception networks should also be performed for these networks. My guess is that the reason behind a seemingly 'ad-hoc' design of Inception modules is to reduce the computational footprint of the model (which is not a central motivation of fractal nets). Since this model is directly related to the Inception module due to use of shorter and longer paths without shortcuts, one can easily simplify the Inception design to build a strong baseline e.g. by converting the concatenation operation to a mean operation among equally sized convolution outputs. As an aside, note that Inception networks have already shown that residual networks are not necessary to obtain the best performance [1].  - It should be noted that Residual/Highway architectures do have a type of anytime property, as shown by lesioning experiments in Srivastava et al and Viet et al.  - The architecture specific drop-path regularization is interesting, but is used along with other regularizers such as dropout, batch norm and weight decay and its benefit on its own is not clear.  Overall, it's not clear to me that the experiments clearly demonstrate the utility of the proposed architecture.   [1] Szegedy, Christian, Sergey Ioffe, and Vincent Vanhoucke. "Inception-v4, inception-resnet and the impact of residual connections on learning." arXiv preprint arXiv:1602.07261 (2016).
answer:
en


question:
Correcciones de Formato:  1.	En palabras claves dejar sólo 5 las más relevantes. 2.	Eliminar las siguientes expresiones: •	“De ahí que en el sistema solar desentone la chatarra de los satélites artificiales, o en el sistema circulatorio sanguíneo produzca hemorragia  la incrustación de una cuchilla:” •	“Si  fuera  más definitiva esta dimensión  valdría  una sartén sobre el escritorio  junto al computador. Si no fueran ciertos los sistemas  valdría dentro de este artículo un párrafo del Quijote.” •	“”El gran error garrafal”, con  el que  censuró el hecho de  identificar cada relación (varrel en sus términos) como una clase.”  Correcciones de Contenido: 1.- El titulo debería ser “Una propuesta de Modelo de Datos para…..”  ya que el titulo actual no refleja el contenido del artículo.  2.- Falta fundamentar mejor la idea de lo que se propone, es decir, realizar un análisis “claro” de las  ventajas  de por qué es interesante e importante la propuesta. Por ejemplo, se puede hacer una tabla con las ventajas sobre otros modelos, por ejemplo, de base de datos relacionales, orientadas a objetos y  distribuidas,…, como los discutidos en los trabajos relacionados.  3.- Debe basarse en un modelo que exista para extender al modelo propuesto y su notación. Actualmente no es claro en cuál es la base para la propuesta.  4.- Si el objetivo es el análisis ¿por qué no sirve una base de datos multidimensional?  5.- En las conclusiones se indica que “En otras palabras,  el MED  es un modelo conceptual  a la medida de bases de datos distribuidas.” Esta afirmación es incorrecta debido a que no se muestra a MED modelado conceptualmente  ¿qué notación conceptual es la que se muestra?.  Debe corregir esto indicando claramente cuál es la notación a ocupar.  6.- En el caso de estudio de la página www.cootracolta.com no hay nada visible por lo cual quizás no sea bueno indicar dicha página web.  7.- El artículo debería seguir los patrones de los artículos que han propuesto modelos de datos por ejemplo cuando se propuso el modelo orientado a objetos GOLD para modelamiento multidimensional.
answer:

es