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:
This paper proposes an approach for multi-lingual named entity recognition using features from Wikipedia. By relying on a cross-lingual Wikifier, it identifies English Wikipedia articles for phrases in a target language and uses features based on the wikipedia entry. Experiments show that this new feature helps not only in the monolingual case, but also in the more interesting direct transfer setting, where the English model is tested on a target language. I liked this paper. It proposes a new feature for named entity recognition and conducts a fairly thorough set of experiments to show the utility of the feature. The analysis on low resource and the non-latin languages are particularly interesting. But what about named entities that are not on Wikipedia? In addition to the results in the paper, it would be interesting to see results on how these entities are affected by the proposed method. The proposed method is strongly dependent on the success of the cross-lingual wikifier. With this additional step in the pipeline, how often do we get errors in the prediction because of errors in the wikifier? Given the poor performance of direct transfer on Tamil and Bengali when lexical features are added, I wonder if it is possible to regularize the various feature classes differently, so that the model does not become over-reliant on the lexical features.
answer:
en

question:
El articulo presenta el diseno de un prototipo que implementa un sistema para la adquisicion de datos mediante dispositivos moviles inalambricos, aplicado a la mineria, especificamente en la monitorizacion de material particulado. Resulta interesante la aplicacion de la tecnologia movil a un problema concreto de la industria minera chilena, ademas el articulo describe de forma muy clara la problematica y el prototipo propuesto. El articulo resulta novedoso ya que se plantea el uso de la computacion (procesamiento de de datos) y de las tecnologias de dispositivos moviles aplicadas a un problema concreto de la industria minera chilena. El problema, la tecnologia presentada (BUG) y el prototipo propuesto estan descritos de forma clara. Resulta relevante la aplicacion de tecnologias computacionales moviles actuales en sectores industriales importantes del pais. El titulo principal y el resumen deben estar escritos en espanol y en ingles (solo vienen en espanol). Ademas, el articulo presenta una lista de 29 referencias, pero solo hace referencia a una (la primera de la lista). Incluso aparece en las Referencias PCFactory, y no queda muy claro el porque.
answer:
es

question:
In this paper, the authors explicitly design geometrical structure into a CNN by combining it with a Scattering network. This aids stability and limited-data performance. The paper is well written, the contribution of combining Scattering and CNNs is novel and the results seem promising. I feel that such work was a missing piece in the Scattering literature to make it useful for practical applications. I wish the authors would have investigated the effect of the stable bottom layers with respect to adversarial examples. This can be done in a relatively straightforward way with software like cleverhans [1] or deep fool [2]. It would be very interesting if the first layer's stability in the hybrid architectures increases robustness significantly, as this would tell us that these fooling images are related to low-level geometry. Finding that this is not the case, would be very interesting as well. Further, the proposed architecture is not evaluated on real limited data problems. This would further strengthen the improved generalization claim. However, I admit that the Cifar-100 / Cifar-10 difference already seems like a promising indicator in this regard. If one of the two points above will be addressed in an additional experiment, I would be happy to raise my score from 6 to 7. Summary: + An interesting approach is presented that might be useful for real-world limited data scenarios. + Limited data results look promising. - Adversarial examples are not investigated in the experimental section. - No realistic small-data problem is addressed. Minor: - The authors should add a SOTA ResNet to Table 3, as NiN is indeed out of fashion these days. - Some typos: tacke, developping, learni. [1]
answer:
en