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: This paper shows that a deep RL approach augmented with auxiliary tasks improves performance on navigation in complex environments. Specifically, A3C is used for the RL problem, and the agent is simultaneously trained on an unsupervised depth prediction task and a self-supervised loop closure classification task. While the use of auxiliary tasks to improve training of models including RL agents is not new, the main contribution here is the use of tasks that encourage learning an intrinsic representation of space and movement that enables significant improvements on maze navigation tasks. The paper is well written, experiments are convincing, and the value of the auxiliary tasks for the problem are clear. However, the contribution is relatively incremental given previous work on RL for navigation and on auxiliary tasks. The work could become of greater interest provided broader analysis and insights on either optimal combinations of tasks for visual navigation (e.g. the value of other visual / geometry-based tasks), or on auxiliary tasks with RL in general. As it is, it is a useful demonstration of the benefit of geometry-based auxiliary tasks for navigation, but of relatively narrow interest.

Answer: en

Question: - No existen bases cientificas explicitas en este trabajo y su aporte a la practica es muy limitado. - Adicionalmente, se debe revisar cuidadosamente su redaccion y la logica de la redaccion. Por ejemplo se dice "altura" cuando se deberia decir magnitud. - Es necesario que los datos revisados sean evaluados en relacion al contexto, les recomiendo a los autores leer sobre analisis similares en reino unido para que puedan explicar sus resultados. - Si es mejorado podria ser presentado en workshop de aplicaciones empresariales.

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