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].

Example Input:  It has recently come to my attention that the objective proposed by the authors in this paper has in fact already been studied in the literature under the name 'reward-weighted regression', from e.g. ICML 2007 [1]. This has spawned several other works using the same objective (e.g. [2]). One can examine for instance the objective proposed in Section 3.4 from [2], from ICANN 2008.  While this paper has already been accepted to ICLR, it would be beneficial for the authors to at least cite these works (and other related works) so that readers are aware of the previous origins of this idea.  [1] 
Example Output: en

Example Input: This paper essentially presents a new inductive bias in the architecture of (convolutional) neural networks (CNN). The mathematical motivations/derivations of the proposed architecture are detailed and rigorous. The proposed architecture promises to produce equivariant representations with steerable features using fewer parameters than traditional CNNs, which is particularly useful in small data regimes. Interesting and novel connections are presented between steerable filters and so called “steerable fibers”. The architecture is strongly inspired by the author’s previous work, as well as that of “capsules” (Hinton, 2011). The proposed architecture is compared on CIFAR10 against state-of-the-art inspired architectures (ResNets), and is shown to be superior particularly in the small data regime. The lack of empirical comparison on large scale dataset, such as ImageNet or COCO makes this largely a theoretical contribution. I would have also liked to see more empirical evaluation of the equivariance properties. It is not intuitively clear exactly why this architecture performs better on CIFAR10 as it is not clear that capturing equivariances helps to classify different instances of object categories. Wouldn’t action-recognition in videos, for example, not be a better illustrative dataset?
Example Output: en

Example Input: I reviewed the manuscript as of December 6th.  The authors perform a systematic investigation of various retraining methods for making a classification network robust to adversarial examples. The authors achieve lower error rates using their RAD and IAEC methods perform better then previously introduced distillation methods for retraining networks to be robust to adversarial examples. This method suggests a promising direction for building a defense for adversarial examples.  Major Comments: I find the paper to not be lacking in exposition and clarity. The paper has a laundry list of related results (page 2) but no clear message. I *think* one larger point is the superior performance of their retraining techniques but it is not clear how well these techniques perform compared to other retraining techniques, nor are the details of the retraining techniques clear. The paper requires more discussion and a clear exposition about the methods the authors introduced (i.e. RAD, IAEC). What follow are some more detailed comments along this theme of improving the exposition and clarity:  - The authors should provide more details about how they constructed the auto-encoder in the IAEC method (diagram?). The same needs to be said for the RAD method. The authors point to a previous workshop submission (
Example Output:
en