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 evaluating topic quality based on using word embeddings to calculate similarity (either directly or indirectly via matrix factorisation), achieving impressive results over standard datasets.  The proposed method represents a natural but important next step in the evolutionary path of research on topic evaluation. The thing that troubled me most with the results was that, while you achieve state-of-the-art results for all three datasets, there are large inconsistencies in which methods perform and which methods perform less well (below the state of the art). In practice, none of the proposed methods consistently beats the state of the art, and the SVD-based methods perform notably badly over the genomics dataset. For someone who wants to take your method off the shelf and use it over any arbitrary dataset, this is a considerable worry. I suspect that the lower results for SVD over genomics relate to the proportion of OOV terms (see comment below), and that it may be possible to automatically predict which method will perform best based on vocab match with GloVe etc., but there is no such discussion in the paper.  Other issues:  - the proposed method has strong similarities with methods proposed in the   lexical chaining literature, which I would encourage the authors to read up   on and include in any future version of the paper  - you emphasis that your method has no parameters, but the word embedding   methods have a large number of parameters, which are implicit in your   method. Not a huge deal, but worth acknowledging  - how does your method deal with OOV terms, e.g. in the genomics dataset   (i.e. terms not present in the pretrained GloVe embeddings)? Are they simply   ignored? What impact does this have on the method?  Low-level issues:  - in your description of word embeddings in Section 2.1, you implicitly assume   that the length of the vector is unimportant (in saying that cosine   similarity can be used to measure the similarity between two vectors). If   the vectors are unit length, this is unproblematic, but word2vec actually   doesn't return unit-length vectors (the pre-trained vectors have been   normalised post hoc, and if you run word2vec yourself, the vector length is   certainly not uniform). A small detail, but important.  - the graphs in Figure 1 are too small to be readable
Output: 

 en