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].

This manuscript addresses an interesting solution based in machine learning techniques to classify spectra legacy data of the Hubble Space Telescope in order to publish the results and "good" spectra in Internet to scientific community. The manuscript is well-written and results are robust. Experiment is sound and the manuscript seems acceptable in the current form. The work can be short, but very interesting to the Infonor and JCC community.  The main problem of the paper is that it is difficult to compare results for the classifiers utilized. A confusion matrix and parameters for each classifier could clarify results.
en

This work investigates the performance of transfer learning from resource-rich setup (BookTest, CNN/Daily Mail corpora) to low-resource (bAbI, SQuAD benchmarks) settings. Experiments show poor improvements in 0-shot learning. However, when the model is exposed to few training instances some improvements are observed.  The claims made here require a more comprehensive analysis. I criticize the use of bAbI as a low-resource real-world scenario. bAbI is designed as a unit test and is far from representing many natural language phenomena. Thus, the claims related to bAbI can only be weak evidence for questioning transfer learning high-resource to low-resource in real-world scenarios. I highly recommend using recently proposed real-world scenarios [1,2].  More importantly, the work does not explain why and how do we get improvement using transfer learning. They remotely address this by hypothesizing the knowledge of transfer is not just encoded in embeddings but also in the model. Considering the related work [3], these claims bring a marginal novelty and still "how and why" should be central in this work.    [1]
en

El documento describe una forma de aplicar técnicas actuales de ingeniería informática al problema de líneas de productos de software. El documento está bien explicado, pero se considera necesario profundizar en los resultados y la evaluación. El trabajo descrito es interesante ya que aplica Algoritmos Genéticos a un problema concreto, pero carece de  impacto innovador en el dominio. Se recomienda revisar la redacción (en general) del documento.

es