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].

Example Input: 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]
Example Output: en

Example Input: El artículo esta adecuadamente escrito, excepto por algunos problemas de acentos (implementara, utilizaran, ...) Las herramientas utilizadas, así como los algoritmos implementados se encuentran adecuadamente descritos. La única crítica esta en la implementación de los mismos, realizada en una única máquina (seguramente por un problema de recursos), algo que quita méritos a lo realizado, considerando que se esta hablando de técnicas pensadas para ser usadas en forma distribuida (donde debería estar su máximo aprovechamiento).  Pese a lo anterior, pienso que es un trabajo bien presentado, valioso en la discusión de las limitaciones de Hadoop vs Giraph, y cuyos resultados son interesantes de conocer y debatir en el ámbito del congreso.
Example Output: es

Example Input: 1) Summary  This paper proposes an end-to-end hybrid architecture to predict the local linear trends of time series. A temporal convnet on raw data extracts short-term features. In parallel, long term representations are learned via a LSTM on piecewise linear approximations of the time series. Both representations are combined using a MLP with one hidden layer (in two parts, one for each stream), and the entire architecture is trained end-to-end by minimizing (using Adam) the (l2-regularized) euclidean loss w.r.t. ground truth local trend durations and slopes.   2) Contributions  + Interesting end-to-end architecture decoupling short-term and long-term representation learning in two separate streams in the first part of the architecture. + Comparison to deep and shallow baselines.  3) Suggestions for improvement  Add a LRCN baseline and discussion: The benefits of decoupling short-term and long-term representation learning need to be assessed by comparing to the popular "long-term recurrent convolutional network" (LRCN) of Donahue et al (
Example Output:
en