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].

Example Input: El articulo presenta un estudio de la percepcion que tienen los ingenieros de requisitos (noveles y expertos) en relacion a la efectividad de las diferentes tecnicas de educcion para el descubrimiento de los requisitos en un proyecto de desarrollo de software. El articulo trata un tema que merece cierta atencion ante la gran cantidad de tecnicas de educcion que existen y la desorientacion de los ingenieros de requisitos respecto de cual utilizar en un determinado contexto. El estudio realizado considera como noveles Ingenieros que no tienen conocimiento de tecnicas de educcion y uno de los atributos que debe ser calificado como base del estudio comparativo, es precisamente su experiencia. De hecho como conclusion se expresa que "una mas amplia formacion y principalmente de tipo practica, es necesaria para que los ingenieros noveles puedan decidir.", eso es de Perogrullo. Deberia ser considera una gama mayor de atributos del contexto como los sugeridos en [14], que permitan conclusiones mas veraces.
Example Output: es

Example Input: El articulo presenta principalmente una aplicacion movil y hay poco de investigacion - Indica que es un trabajo de "tesis" - Falta resumen en espanol y palabras clave - Las figuras no deben ir en colores (segun formato) - Poner un nombre mas adecuado a la Tabla 2. - Poner acento a "significara", "perdida" - Ajustar el texto a los margenes - Leyendas como "Figura6" aparecen sin espacio en blanco
Example Output: es

Example Input: This work reframes paragraph vectors from a generative point of view and in so doing, motivates the existing method of inferring paragraph vectors as well as applying a L2 regularizer on the paragraph embeddings. The work also motivates joint learning of a classifier on the paragraph vectors to perform text classification. The paper has numerous citation issues both in formatting within the text and the formatting of the bibliography, e.g. on some occasions including first names, on others not. I suggest the authors use a software package like BibTex to have a more consistent bibliography. There seems to be little novelty in this work. The authors claim that there is no proposed method for inferring unseen documents for paragraph vectors. This is untrue. In the original paragraph vector paper, the authors show that to get a new vector, the rest of the model parameters are held fixed and gradient descent is performed on the new paragraph vector. This means the original dataset is not needed when inferring a paragraph vector for new text. This work seems to be essentially doing the same thing when finding the MAP estimate for a new vector. Thus the only contribution from the generative paragraph vector framing is the regularization on the embedding matrix. The supervised generative paragraph vector amounts to jointly training a linear classifier on the paragraph vectors, while inference for the paragraph vector is unchanged. For the n-gram based approach, the authors should cite Li et al., 2015. In the experiments, table 1 and 2 are badly formatted with .0 being truncated. The authors also do not state the size of the paragraph vector. Finally the SGPV results are actually worse than that reported in the original paragraph vector paper where SST-1 got 48.7 and SST-2 got 86.3. Bofang Li, Tao Liu, Xiaoyong Du, Deyuan Zhang, Zhe Zhao, Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews, 2015.
Example Output:
en