Definition: 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].

Positive Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: es

Positive Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: en

Negative Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: en

Negative Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: es

Now complete the following example -
Input: This paper addresses an important and timely topic in a creative way. I consider it to have three flaws (and one good idea).  1) insufficient context of what is known and had been studied before (in shallow RL), for example within the field of “robust RL”. A good place to start might be with the work of Shie Mannor.  2) an ill-defined general problem setup. Does it make sense to do post-hoc labeling of certain actions as “catastrophic” if the agent is not informed about that metric during learning? Training a system to do one thing (maximize reward), but then evaluating it with a different metric is misleading. On the training metric, it could even be that the baseline outperforms the new algorithm? So I’d want to see plots for “average reward” in fig 3 as well. Also, what would the baseline learn if it was given large negative rewards for entering these otherwise invisible “danger states”?  3) a somewhat ad-hoc solution, that introduces new domain-specific hyperparameters (k_r, k_lambda and lambda) a second deep network and and two additional replay memories. In terms of results, I’m also unsure whether I can trust the results, given the long-standing track-record of cart-pole being fully solved by many methods: is DQN an outlier here? Or is the convnet not an appropriate function-approximator? Actually: which exact variant “state-of-the-art” variant of DQN are you using?  The good idea that I encourage the authors to pursue further is D_d, this set of rare but dangerous states, that should be kept around in some form. I see it as an ingredient for continual learning that most typical methods lack -- it is also one of the big differences between RL and supervised learning, where such states would generally be discarded as outliers.  Given my comment a couple of weeks ago, and the prompt response (“we implemented expected SARSA”), I would have expected that the paper had been revised with the new results by now? In any case, I’m open to discussing all these points and revising my opinion based on an updated version of the paper.  Minor comment: the bibliography is done sloppily, with missing years, conference venues and missing/misspelled author lists, e.g. “Sergey et al. Levine”. I also think it is good form to cite the actual conference publications instead of arXiv where applicable.
Output: 

 en