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].

Ex Input:
Problemas de Fondo: Los autores presentan los resultados de un proyecto de interoperabilidad entre plataformas, definen los estándares para la creación de contenido, sin embargo, los argumentos utilizados para seleccionar el modelo carecen de validación.  Se esperaría al menos la validación de dos o más plataformas antes de definir y concluir que lo propuesto es la mejor selección.  La encuesta aplicada a 50 personas no describe en que plataformas se realizó y a qué tipo de público objetivo.   Problemas de Forma: - Palabras sin tilde: último, validó, módulo. - Inicio de frases sin mayúsculas. - No se respeta el márgen en la tabla. - Los títulos de las figuras se encuentran en páginas diferentes. - La enumeración de los dispositivos es confusa y sin formato.

Ex Output:
es


Ex Input:
-	No existen bases científicas explicitas en este trabajo y su aporte a la práctica es muy limitado. -	Adicionalmente, se debe revisar cuidadosamente su redacción y la lógica de la redacción. Por ejemplo se dice "altura" cuando se debería decir magnitud. -	Es necesario que los datos revisados sean evaluados en relación al contexto, les recomiendo a los autores leer sobre análisis similares en reino unido para que puedan explicar sus resultados. -	Si es mejorado podría ser presentado en workshop de aplicaciones empresariales.

Ex Output:
es


Ex Input:
This paper proposes a neural architecture for answering non-factoid questions. The author's model improves over previous neural models for answer sentence selection. Experiments are conducted on a Japanese love advice corpus; the coolest part of the paper for me was that the model was actually rolled out to the public and its answers were rated twice as good as actual human contributors!   It was hard for me to determine the novelty of the contribution. The authors mention that their model "fills the gap between answer selection and generation"; however, no generation is actually performed by the model! Instead, the model appears to be very similar to the QA-LSTM of Tan et al., 2015 except that there are additional terms in the objective to handle conclusion and supplementary sentences. The structure of the answer is fixed to a predefined template (e.g., conclusion --> supplementary), so the model is not really learning how to order the sentences. The other contribution is the "word embedding with semantics" portion described in sec 4.1, which is essentially just the paragraph vector model except with "titles" and "categories" instead of paragraphs.   While the result of the paper is a model that has actually demonstrated real-life usefulness, the technical contributions do not strike me as novel enough for publication at ICLR.  Other comments: - One major issue with the reliance of the model on the template is that you can't evaluate on commonly-used non-factoid QA datasets such as InsuranceQA. If the template were not fixed beforehand (but possibly learned by the model), you could conceivably evaluate on different datasets.  - The examples in Table 4 don't show a clear edge in answer quality to your model; QA-LSTM seems to choose good answers as well. - Doesn't the construction model have an advantage over the vanilla QA-LSTM in that it knows which sentences are conclusions and which are supplementary? Or does QA-LSTM also get this distinction?

Ex Output:

en