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].

Input: Consider Input: The paper proposes an approach to sequence transduction for the case when a monotonic alignment between the input and the output is plausible. It is assumed that the alignment can be provided as a part of training data, with Chinese Restaurant process being used in the actual experiments.   The idea makes sense, although its applicability is limited to the domains where a monotonic alignment is available. But as discussed during the pre-review period, there has been a lot of strongly overlapping related work, such as probabilistic models with hard-alignment (Sequence Transduction With Recurrent Neural Network, Graves et al, 2012) and also attempts to use external alignments in end-to-end models (A Neural Transducer, Jaitly et al, 2015). That said, I do not think the approach is sufficiently novel.   I also have a concern regarding the evaluation. I do not think it is fair to compare the proposed model that depends on external alignment with the vanilla soft-attention model that learns alignments from scratch. In a control experiment soft-attention could be trained to match the external alignment. Such a pretraining could reduce overfitting on the small dataset, the one on which the proposed approach brings the most improvement. On a larger dataset, especially SIGMORPHON, the improvements are not very big and are only obtained for a certain class of languages.  To sum up, two main issues are (a) lack of novelty (b) the comparison of a model trained with external alignment and one without it.

Output: en


Input: Consider Input: Un buen aporte y mirada al futuro. La redacción presenta algunas dificultades, revisar. Falta dar un ejemplo para algún tipo de dispositivo, habría sido ideal.

Output: es


Input: Consider Input: Se presenta la necesidad de usar Objetos de Aprendizaje (OA) para la enseñanza del curso "Introducción a la Teoría de Autómatas". Sin embargo es muy poca la información dedicada en el artículo a los OA elegidos para el curso, no hay información de resultados de su aplicación ni sus consecuencias (resultados de aprendizaje, encuesta a los alumnos para validarlo, etc.). Es por ello que no se recomienda aceptar el artículo, salvo que se agregue información que respalde la aplicación de OA en este curso.


Output: es