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: The paper proposes a neural approach to learning an image compression-decompression scheme as an auto-encoder. While the idea is certainly interesting and well-motivated, in practice, it turns out to achieve effectively identical rates to JPEG-2000.  Now, as the authors argue, there is some value to the fact that this scheme was learned automatically rather than by expert design---which means it has benefits beyond the compression of natural images (e.g., it could be used to automatically learning a compression scheme for signals for which we don't have as much domain knowledge). However, I still believe that this makes the paper unsuitable for publication in its current form because of the following reasons---  1. Firstly, the fact that the learned encoder is competitive---and not clearly better---than JPEG 2000 means that the focus of the paper should more be about the aspects in which the encoder is similar to, and the aspects in which it differs, from JPEG 2000. Is it learning similar filters or completely different ones ? For what kinds of textures does it do better and for what kinds does it do worse (the paper could show the best and worst 10 patches at different bit-rates) ?  2. Secondly, I think it's crucial that the paper demonstrate that the benefits come from a better coding scheme (as opposed to just a better decoder), as suggested in my initial pre-review question. How would a decoder trained on JPEG-2000 codes (and perhaps also on encoded random projections) do worse or better ?  3. Finally, I think the fact that it does as well/worse than JPEG-2000 significantly diminishes the case for using a 'deep' auto-encoder. JPEG-2000 essentially uses a wavelet transform, which is a basis that past studies have shown could be recovered using a simple sparse dictionary algorithm like K-SVD. This is why I feel that the method needs to clearly outperform JPEG-2000, or show comparisons to (or atleast discuss) a well-crafted traditional/generative model-based baseline.
Output: 

 en