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 is a nice paper on morphological segmentation utilizing word  embeddings. The paper presents a system which uses word embeddings to  both measure local semantic similarity of word pairs with a potential  morphological relation, and global information about the semantic validity of potential morphological segment types. The paper is well written and  represents a nice extension to earlier approaches on semantically driven  morphological segmentation.  The authors present experiments on Morpho Challenge data for three  languages: English, Turkish and Finnish. These languages exhibit varying  degrees of morphological complexity. All systems are trained on Wikipedia  text.   The authors show that the proposed MORSE system delivers clear  improvements w.r.t. F1-score for English and Turkish compared to the well  known Morfessor system which was used as baseline. The system fails to  reach the performance of Morfessor for Finnish. As the authors note, this  is probably a result of the richness of Finnish morphology which leads to  data sparsity and, therefore, reduced quality of word embeddings. To  improve the performance for Finnish and other languages with a similar  degree of morphological complexity, the authors could consider word  embeddings which take into account sub-word information. For example,  @article{DBLP:journals/corr/CaoR16,   author    = {Kris Cao and                Marek Rei},   title     = {A Joint Model for Word Embedding and Word Morphology},   journal   = {CoRR},   volume    = {abs/1606.02601},   year                  = {2016},   url                 = {[Link]},   timestamp = {Fri, 01 Jul 2016 17:39:49 +0200},   biburl    = {[Link]},   bibsource = {dblp computer science bibliography, [Link]} }  @article{DBLP:journals/corr/BojanowskiGJM16,   author    = {Piotr Bojanowski and                Edouard Grave and                Armand Joulin and                Tomas Mikolov},   title     = {Enriching Word Vectors with Subword Information},   journal   = {CoRR},   volume    = {abs/1607.04606},   year                  = {2016},   url                 = {[Link]},   timestamp = {Tue, 02 Aug 2016 12:59:27 +0200},   biburl    = {[Link]},   bibsource = {dblp computer science bibliography, [Link]} }  The authors critique the existing Morpho Challenge data sets.  For example, there are many instances of incorrectly segmented words in  the material. Moreover, the authors note that, while some segmentations  in the the data set may be historically valid (for example the  segmentation of business into busi-ness), these segmentations are no  longer semantically motivated. The authors provide a new data set  consisting of 2000 semantically motivated segmentation of English word  forms from the English Wikipedia. They show that MORSE deliver highly  substantial improvements compared to Morfessor on this data set.  In conclusion, I think this is a well written paper which presents  competitive results on the interesting task of semantically driven  morphological segmentation. The authors accompany the submission with  code and a new data set which definitely add to the value of the  submission.
Output: 

 en