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:
Se trata de un artículo bastante particular que propone una extensión a los modelos de datos existentes considerando la idea de "sistema" (de objetos). Para esto se propone un lenguaje (LATIN) y un modelo de datos ¿universal?, MED, basado en un "volumen esférico" con dos polos "mayor" y "diario" basados en ideas contables. Este es, a mi juicio, el aspecto más crítico de la propuesta, ya que surge la duda, de si todo sistema debería calzar en este esquema, o estamos hablando de un conjunto particular de casos. Las ideas posteriores son interesante y, al menos, generan un quiebre con respecto a las ideas clásicas de la orientación a objetos. Cito la idea de sistema propuesta: "un sistema es un conjunto de clases enlazadas e inter-actuantes según un mismo método, el cual no es particular a cada clase sino emergente de todas ellas". Otras ideas asocian transacción con conjunto referencial, macros, tipos, instancias, ... lo que en términos globales constituyen un nuevo cuerpo de ideas. No habiendo en las referencias alguna cita a algún artículo anterior del autor que trate estas ideas, me parece que puede llegar a ser interesante escuchar esta propuesta y debatir al respecto. En términos de formalidad, el artículo no cumple cabalmente con el formato del congreso, y el número de páginas excede las ocho solicitadas.

Ex Output:
es


Ex Input:
This work describes a gated attention-based recurrent neural network method for reading comprehension and question answering. This method employs a self-matching attention technique to counterbalance the limited context knowledge of gated attention-based recurrent neural networks when processing passages. Finally, authors use pointer networks  with signals from the question attention-based vector to predict the beginning and ending of the answer. Experimental results with the SQuAD dataset offer state-of-the-art performance compared with several recent approaches.   The paper is well-written, structured and explained. As far as I know, the mathematics look also good. In my opinion, this is a very interesting work which may be useful for the question answering community.  I was wondering if the authors have plans to release the code of this approach. From that perspective, I miss a bit of information about the technology used for the implementation (theano, CUDA, CuDNN...), which may be useful for readers.  I would appreciate if authors could perform a test of statistical significance of the results. That would highlight even more the quality of your results.  Finally, I know that the space may be a constraint, but an evaluation including some additional dataset would validate more your work.

Ex Output:
en


Ex Input:
This paper describes a system to assist written test scoring.  - Strengths: The paper represents an application of an interesting NLP problem -- recognizing textual entailment -- to an important task -- written test scoring.  - Weaknesses: There isn't anything novel in the paper. It consist of an application of an existing technology to a known problem.  The approach described in the paper is not autonomous -- it still needs a human to do the actual scoring. The paper lacks any quantitative or qualitative evaluation of how useful such system is. That is, is it making the job of the scorer easier? Is the scorer more effective as compared to not having automatic score?  The system contains multiple components and it is unclear how the quality of each one of them contributes to the overall experience.  The paper needs more work with the writing. Language and style is rough in several places.  The paper also contains several detailed examples, which don't necessarily add a lot of value to the discussion.   For the evaluation of classification, what is the baseline of predicting the most frequent class?  - General Discussion: I find this paper not very inspiring. I don't see the message in the paper apart from announcing having build such a system

Ex Output:

en


