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 paper evaluates several strategies to reduce output vocabulary size in order to speed up NMT decoding and training. It could be quite useful to practitioners, although the main contributions of the paper seem somewhat orthogonal to representation learning and neural networks, and I am not sure ICLR is the ideal venue for this work. - Do the reported decoding times take into account the vocabulary reduction step? - Aside from machine translation, might there be applications to other settings such as language modeling, where large vocabulary is also a scalability challenge? - The proposed methods are helpful because of the difficulties induced by using a word-level model. But (at least in my opinion) starting from a character or even lower-level abstraction seems to be the obvious solution to the huge vocabulary problem.
en

Problemas de Fondo: Los autores presentan los resultados de un proyecto de interoperabilidad entre plataformas, definen los estandares para la creacion de contenido, sin embargo, los argumentos utilizados para seleccionar el modelo carecen de validacion. Se esperaria al menos la validacion de dos o mas plataformas antes de definir y concluir que lo propuesto es la mejor seleccion. La encuesta aplicada a 50 personas no describe en que plataformas se realizo y a que tipo de publico objetivo. Problemas de Forma: - Palabras sin tilde: ultimo, valido, modulo. - Inicio de frases sin mayusculas. - No se respeta el margen en la tabla. - Los titulos de las figuras se encuentran en paginas diferentes. - La enumeracion de los dispositivos es confusa y sin formato.
es

- Strengths: The approach described in the manuscript outperformed the previous approaches and achieved the state-of-the-art result. Regarding data, the method used the combination of market and text data. The approach used word embeddings to define the weight of each lexicon term by extending it to the similar terms in the document. - Weaknesses: Deep-learning based methods were known to be able to achieve relatively good performances without much feature engineering in sentimental analysis. More literature search is needed to compare with the related works would be better. The approach generally improved performance by feature-based methods without much novelty in model or proposal of new features. - General Discussion: The manuscript described an approach in sentimental analysis. The method used a relatively new method of using word embeddings to define the weight of each lexicon term. However, the novelty is not significant enough.
en