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]: Ese trabajo muestra el procedimiento propuesto para la deteccion automatica de fracturas en pozos. La metodologia se ve novedosa e interesante. El articulo muestra bien la metodologia y tiene una buena redaccion, pero al finalizar el articulo falta una mejor presentacion de los resultados en pozos reales. No queda claro el aporte del articulo, primero indica que es la deteccion automatica y en otra parte indica que es la modificacion al algoritmo. Debe mejorar la presentacion de los resultados en pozos reales y compararlo con lo deseado y quizas con resultados de aplicar otras tecnicas.
[A]: es

[Q]: This paper introduces a hierarchical clustering method using learned CNN features to build 'the tree of life'. The assumption is that the feature similarity indicates the distance in the tree. The authors tried three different ways to construct the tree: 1) approximation central point 2) minimum spanning tree and 3) multidimensional scaling based method. Out of them, MDS works the best. It is a nice application of using deep features. However, I lean toward rejecting the paper because the following reasons: 1) All experiments are conducted in very small scale. The experiments include 6 fish species, 11 canine species, 8 vehicle classes. There are no quantitative results, only by visualizing the generated tree versus the wordNet tree. Moreover, the assumption of using wordNet is not quite valid. WordNet is not designed for biology purpose and it might not reflect the true evolutionary relationship between species. 2) Limited technical novelty. Most parts of the pipeline are standard, e.g. use pretrained model for feature extraction, use previous methods to construct hierarchical clustering. I think the technical contribution of this paper is very limited.
[A]: en

[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