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 an "interactive" version of the bAbI dataset by adding supporting questions/answers to the dataset in cases where there is not enough information to answer the question. Interactive QA is certainly an interesting problem and is well-motivated by the paper. However, I don't feel like the bAbI extension is adequately explained. For example, the baseline DMN and MemN2N models on the IQA task are "take both statements and question as input and then estimate an answer." Their task is then fundamentally more difficult from the CAN's because they do not distinguish "feedback" from the original context; perhaps a more fair approach would be to treat **every** question (both supporting and original questions) as individual instances. Also, how were the supporting questions and the user feedback generated? How many templates / words were used to create them? The dataset creation details are missing, and if space is an issue, a lot of basic exposition on things like GRU / sentence encodings can be cut (or at least greatly shortened) and replaced with pointers to the original papers.   Another issue I had is that the model attempts to generate these synthetic questions; if there are just one or two templates, why not just predict the values that fill these templates? So instead of generating "Which bedroom, master one or guest one?" with an RNN decoder, just predict "which" or "which bedroom"... isn't this sufficient? In the end, these just seem like more supporting facts, not actual interaction with users, and the fact that it is run on only three of the original twenty tasks make the conclusions hard to trust.  In conclusion, I think the paper has a strong idea and motivation, but the experiments are not convincing for the paper to be accepted at ICLR.
Output: 

 en