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: Thank you for an interesting read.  I found this paper very interesting. Since I don't think (deterministic) approximate inference is separated from the modelling procedure (cf. exact inference), it is important to allow the users to select the inference method to suit their needs and constraints. I'm not an expert of PPL, but to my knowledge this is the first package that I've seen which put more focus on compositional inference. Leveraging tensorflow is also a plus, which allows flexible computation graph design as well as parallel computation using GPUs.  The only question I have is about the design of flexible objective functions to learn hyper-parameters (or in the paper those variables associated with delta q distributions). It seems hyper-parameter learning is also specified as inference, which makes sense if using MAP. However the authors also demonstrated other objective functions such as Renyi divergences, does that mean the user need to define a new class of inference method whenever they want to test an alternative loss function?

Answer: en


Question: -	El tema es de relevancia científica. -	En el trabajo se desarrolló un análisis de la literatura sobre calidad de datos en las aplicaciones de e-government municipal. -	Metodológicamente existen tres limitaciones importantes a superar. Primero, no se ha considerado el factor de impacto de la publicaciones, con lo cual se ha desestimado el desarrollo asociado a este tipo de revisiones, mezclando trabajos de alto impacto (ejemplo, artículos de revistas ISIs factor de impacto >2, como MISQ) con trabajos sin una revisión de calidad (ejemplo, lo que Scholar "lanza" sólo por coincidir en la búsqueda). Segundo, se dice que se tomarán referencias del 2008 en adelante prioritariamente, pero muchas de sus referencias  son de antes de esa fecha, y eso no es raro, pues entre 2001 y 2004 hay una explosión de trabajos en este ámbito específico. Tercero, no se han incluido explícitamente trabajos de conferencias del área de alto impacto en la comunidad científica como AMCIS o ECIS (la primera tiene una base de datos privada, no accesible por google y la segunda es libre). -	Las limitaciones antes expuestas restringen los alcances de los interesantes  resultados expuestos en el trabajo. -	Finalmente, se sugiere avanzar en las escalas de medida de dimensiones seleccionadas  antes de desarrollar caso de estudio indicado en las conclusiones.

Answer: es


Question: this work investigates a joint learning setup where tasks are stacked based on their complexity. to this end, experimental evaluation is done on pos tagging, chunking, dependency parsing, semantic relatedness, and textual entailment. the end-to-end model improves over models trained solely on target tasks.  although the hypothesis of this work is an important one, the experimental evaluation lacks thoroughness:  first, a very simple multi-task learning baseline [1] should be implemented where there is no hierarchy of tasks to test the hypothesis of the tasks should be ordered in terms of complexity.  second, since the test set of chunking is included in training data of dependency parsing, the results related to chunking with JMT_all are not informative.   third, since the model does not guarantee well-formed dependency trees, thus, results in table 4 are not fair.   minor issue: - chunking is not a word-level task although the annotation is word-level. chunking is a structured prediction task where we would like to learn a structured annotation over a sequence [2].  [1]

Answer:
en