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:
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:
Strengths  - interesting to explore the connection between ReLU DNN and simplified SFNN - small task (MNIST)  is used to demonstrate the usefulness of the proposed training methods experimentally - the proposed, multi-stage training methods are simple to implement (despite lacking theoretical rigor)   Weaknesses  -no results are reported on real tasks with large training set  -not clear exploration on the scalability of the learning methods when training data becomes larger  -when the hidden layers become stochastic, the model shares uncertainty representation with deep Bayes networks or deep generative models (Deep Discriminative and Generative Models for Pattern Recognition , book chapter in “Pattern Recognition and Computer Vision”, November 2015, Download PDF). Such connections should be discussed, especially wrt the use of uncertainty representation to benefit pattern recognition (i.e. supervised learning via Bayes rule) and to benefit the use of domain knowledge such as “explaining away”.  -would like to see connections with variational autoencoder models and training, which is also stochastic with hidden layers

Ex Output:
en


Ex Input:
El artículo presenta una aplicación para celulares que ayudaría al ciudadano mexicano a mejorar la gestión del pago de los impuestos federales. El principal problema de este artículo es que no muestra  el uso de técnicas de construcción de softwares ni esgrime detalles de la propia solución (la arquitectura del software).  Un segundo punto más fuerte para mi evaluación negativa, es la pésima calidad del texto. Por ejemplo:  1) Errores tipográficos, como por ejemplo "fiscas" en el resumen  2) Frases largas y confusas como en el primer parágrafo de la introducción y las dos últimas de la columna  izquierda en la página 2.  3) Frases que no respetan la concordancia de género y número como por ejemplo "la investigaciones está organizado" en la página 2.  4) El cuadro 2 es citado antes del cuadro 1, no existe un cuadro 3, en el cuadro 4 los porcentajes están errados, y  el cuadro 5 además de no ser citado en el texto, contiene intervalos muy pequeños (podría juntar los dos intervalos de edad intermediarios) y trabaja con edades fraccionadas...es mejor tener porcentajes menos redondos que edades fraccionadas.  5) El texto solo tiene dos secciones: Introducción y Conclusión (que ellos llamaron de reflexiones).  6) Las referencias no siguen un padrón único.

Ex Output:

es