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 proposes a method for generating datasets of pictures from simple building blocks, as well as corresponding logical forms and language descriptions. The goal seems to be to have a method where the complexity of pictures and corresponding desciptions can be controlled and parametrized.    - The biggest downside seems to be that the maximally achievable complexity is very limited, and way below the complexity typically faced with image-captioning and other multimodal tasks.   - The relative simplicity is also a big difference to the referenced bAbI tasks (which cover the whole qualitative spectrum of easy-to-hard reasoning tasks), whereas in the proposed method a (qualitatively) easy image reconition task can only be quantitatively made harder, by increasing the number of objects, noise etc in unnatural ways.  - This is also reflected in the experimental section. Whenever the experimental performance results are not satisfying, these cases seem like basic over/underfitting issues that may easily be tackled by restricting/extending the capacity of the networks or using more data. It is hard for me to spot any other qualitative insight.  - In the introduction it is stated that the "goal is not too achieve optimal performance" but to find out whether "architectures are able to successfully demonstrate the desired understanding" - there is a fundamental contradiction here, in that the proposed task on the one side is meant to provide a measure as to whether architectures demontrate "understanding", on the other hand the score is not supposed to be taken as meaningful/seriously.  General comments: The general approach should be made more tangible earlier (i.e. in the introction rather than in section 3)
Output: 

 en