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].

Example Input: The idea of building a graph-based differentiable memory is very good. The proposed approach is quite complex, but it is likely to lead to future developments and extensions. The paper has been much improved since the original submission. The results could be strengthened, with more comparisons to existing results on bAbI and baselines on the experiments here. Exploring how it performs with less supervision, and different types of supervision, from entirely labeled graphs versus just node labels, would be valuable.
Example Output: en

Example Input: 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.
Example Output: es

Example Input: This paper presents a dialogue agent where the belief tracker and the dialogue manager are jointly optimised using the reinforce algorithm. It learns from interaction with a user simulator. There are two training phases. The first is an imitation learning phase where the system is initialised using supervising learning from a rule-based model. Then there is a reinforcement learning phase where the system has jointly been optimised using the RL objective. - Strengths: This paper presents a framework where a differentiable access to the KB is integrated in the joint optimisation. This is the biggest contribution of the paper. - Weaknesses: Firstly, this is not a truly end-to-end system considering the response generation was handcrafted rather than learnt. Also, their E2E model actually overfits to the simulator and performs poorly in human evaluation. This begs the question whether the authors are actually selling the idea of E2E learning or the soft-KB access. The soft-KB access actually brings consistent improvement, however the idea of end-to-end learning not so much. The authors tried to explain the merits of E2E in Figure 5 but I also fail to see the difference. In addition, the authors didn't motivate the reason for using the reinforce algorithm which is known to suffer from high variance problem. They didn't attempt to improve it by using a baseline or perhaps considering the natural actor-critic algorithm which is known to perform better. - General Discussion: Apart from the mentioned weaknesses, I think the experiments are solid and this is generally an acceptable paper. However, if they crystallised the paper around the idea which actually improves the performance (the soft KB access) but not the idea of E2E learning the paper would be better.
Example Output:
en