instruction:
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].
question:
Los autores proponen el análisis de los datos a través de la metodología CRISP-DM, pero en el desarrollo no se utiliza el mismo.  Los autores hacen uso de la herramienta WEKA y de los algoritmo que tiene implementados dicha herramienta. Tal vez fuera mejor pensar en un algoritmo propio para este tipo de datos a analizar, o alguna mecanismo donde el experto no tenga que marcar las formas de los remolinos en las imágenes a procesar.
answer:
es


question:
Quality, Clarity: There is no consensus on this, with the readers having varying backgrounds, and one reviewer commenting that they found it to be unreadable.     Originality, Significance:   The reviews are mixed on this, with the high score (7) acknowledging a lack of expertise on program induction.  The paper is based on the published TerpreT system, and some think that it marginal and contradictory with respect to the TerpreT paper. In the rebuttal, point (3) from the authors points to the need to better understand gradient-based program search, even if it is not always better. This leaves me torn about a decision on this paper, although currently it does not have strong support from the most knowledgeable reviewers.  That said, due to the originality of this work, the PCs are inclined to invite this work to be presented as a workshop contribution.
answer:
en


question:
I like the setting presented in this paper but I have several criticism/questions:  (1) What are the failure model of this work? As richness of behaviors get complex, I expected this approach to have issues with the diversity of skills that could be discovered.  (2) Looking at Sec 5.3 -- " let X be a random variable denoting the grid in which the agent is currently situated" -- is the space discretized? And if so why and what happens if it isn't.   (3) Expanding on the first point, does the approach work with more complicated embodiment? Say a 5-link swimmer instead of 2? I think this is important to assess the generality of this approach  (4) Authors claim that "Recently, Heess et al. (2016) have independently proposed to learn a range of skills in a pre-training environment that will be useful for the downstream tasks, which is similar to our framework. However, their pre-training setup requires a set of goals to be specified. In comparison, we use intrinsic rewards as the only signal to the agent during the pre-training phase, the construction of which only requires very minimal domain knowledge."  I don't entirely agree with this. The rewards that this paper proposes are also quite hand-crafted and specific to a seemingly limited set of control tasks.
answer:

en