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].
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Question: El articulo describe una propuesta de arquitectura para sistemas abiertos en el contexto de agentes inteligentes. Aborda el aspecto teorico un determinado tipo de agentes en forma adecuada, dejando fuera el tipo de agentes recomendadores. La propuesta esta bien descrita, detallada, y formalizada, lo que deja de manifiesto un trabajo interesante para ser presentado. EL detalle va solamente por el lado de la evaluacion, quizas muy acotada y con poco desarrollo, considero que probar la arquitectura con un numero reducido (50) requerimientos es muy poco para tener una evaluacion empirica ad-hoc de su desempeno. En la forma, hay pequenos detalles ortograficos, puntuacion y la figura 1 no es referenciada en al articulo. Tambien hay titulos en ingles como el II b y el II a. Finalmente, llama la atencion la seccion de agradecimientos que esta en ingles y da a a entender que el trabajo esta inserto en un proyecto financiado por Conicyt.

Answer: es

Question: This paper presents a method to learn both a model and inference procedure at the same time with recurrent neural networks in the context of inverse problems. The proposed method is interesting and results are quite good. The paper is also nicely presented. I would be happy to see some discussion about what the network learns in practice about natural images in the case of denoising. What are the filters like? Is it particularly sensitive to different structures in images? edges? Also, what is the state in the recurrent unit used for? when are the gates open etc. Nevertheless, I think this is nice work which should be accepted.

Answer: en

Question: Thank you for an interesting read on an approach to choose computational models based on kind of examples given. Pros - As an idea, using a meta controller to decide the computational model and the number of steps to reach the conclusion is keeping in line with solving an important practical issue of increased computational times of a simple example. - The approach seems similar to an ensemble learning construct. But instead of random experts and a fixed computational complexity during testing time the architecture is designed to estimate hyper-parameters like number of ponder steps which gives this approach a distinct advantage. Cons - Even though the metacontroller is designed to choose the best amongst the given experts, its complete capability has not been explored yet. It would be interesting to see the architecture handle more than 2 experts.

Answer:
en