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].

[Q]: This paper presents several weakly supervised methods for developing NERs. The methods rely on some form of projection from English into another language. The overall approach is not new and the individual methods proposed are improvements of existing methods. For an ACL paper I would have expected more novel approaches.  One of the contributions of the paper is the data selection scheme. The formula used to calculate the quality score is quite straightforward and this is not a bad thing. However, it is unclear how the thresholds were calculated for Table 2. The paper says only that different thresholds were tried. Was this done on a development set? There is no mention of this in the paper. The evaluation results show clearly that data selection is very important, but one may not know how to tune the parameters for a new data set or a new language pair.   Another contribution of the paper is the combination of the outputs of the two systems developed in the paper. I tried hard to understand how it works, but the description provided is not clear.   The paper presents a number of variants for each of the methods proposed. Does it make sense to combine more than two weakly supervised systems? Did the authors try anything in this direction.  It would be good to know a bit more about the types of texts that are in the "in-house" dataset.
[A]: en


[Q]: Ese trabajo muestra el procedimiento propuesto para la Identificación automática de fracturas en imágenes de pozos. La metodología se ve novedosa e interesante. El artículo muestra bien la metodología y tiene una buena redacción. Se agregaron métricas para medir la efectividad del algoritmo.  Este trabajo fue presentado en INFONOR 2014 y la redacción actual es la misma, que si bien es cierto es buena, presenta los mismos pequeños errores que en el artículo anterior. Por lo tanto, debe volver a revisarlo.  Aunque ahora muestra métricas para medir el resultado no se indica qué tan efectivo es este sistema respecto al utilizado antes.  Debe mejorar las figuras 12 y 15, en donde muestras las métricas. La figura 2, izquierda no se distingue. Falta incluir una referencia a la figura 11 en el texto.
[A]: es


[Q]: SUMMARY.  The paper propose a new scoring function for knowledge base embedding. The scoring function called TransGaussian is an novel take on (or a generalization of) the well-known TransE scoring function. The proposed function is tested on two tasks knowledge-base completion and question answering.  ----------  OVERALL JUDGMENT While I think this proposed work is very interesting and it is an idea worth to explore further, the presentation and the experimental section of the paper have some problems. Regarding the presentation, as far as I understand this is not an attention model as intended standardly in the literature. Plus, it has hardly anything to share with memory networks/neural Turing machines, the parallel that the authors try to make is not very convincing. Regarding the experimental section, for a fair comparison the authors should test their model on standard benchmarks, reporting state-of-the-art models. Finally, the paper lack of discussion of results and insights on the behavior of the proposed model.   ----------  DETAILED COMMENTS   In section 2.2 when the authors calculate \mu_{context} do not they loose the order of relations? And if it is so, does it make any sense?
[A]:
en