Teacher: 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].
Teacher: Now, understand the problem? If you are still confused, see the following example:
Este articulo no es un articulo de investigacion, ya que solo muestra como programar un robot mediante la herramienta de logica difusa. Este tema ya ha sido propuesto como solucion en navegacion de robots.
Solution: es
Reason: This review is written in spanish. Hence, the label is 'es'.

Now, solve this instance: 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.
Student:
en