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: Update: After reading the rebuttal comments and the revised paper, I'm leaving the rating as it was before.  This paper proposes an unsupervised algorithm for transferring samples from one domain to another (related) domain under the constraint that some predefined f returns same result for the input and the result.  Pros: 1. The paper presents an interesting idea of comparing samples from different domains using a fixed perceptual function f.  2. The proposed method produces visually appealing results on several datasets  3. The authors demonstrate how their approach can be used for domain adaptation and obtain improved results on the SVHN->MNIST task  4. The paper is well-written and easy to read  Cons: 1. The novelty of the method is relatively minor (I consider f-constancy term as the main contribution)  2. It feels like the proposed approach would break for more dissimilar domains. The method relies on a fixed f which is trained on the source domain. This f can potentially drop information important for obtaining 1) better reconstructions in the target domain  2) more tightly related x and g(f(x)). I think the authors should consider either training all the modules in the model end-to-end or incorporating target samples into the training of f.  3. A single domain adaptation experiment is definitely not enough to consider the proposed method as a universal alternative to the existing DA approaches.  I would also like to point out that using super-resolved outputs as opposed to the actual model’s outputs can produce a false impression of the visual quality of the transferred samples. I’d suggest moving original outputs from the appendix into the main part.
Output: 

 en