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].

[Q]: El trabajo es DEMASIADO preliminar: no existen experimentos y por tanto sin resultados ni análisis.  El trabajo solo se enfoca en realizar una discusión de métodos de optimización para el problema en cuestión. Posteriormente, solo define formalismos conocidos y genéricos para el problema.   El paper es MUY débil, requiere aún MUCHO trabajo para ser un manuscrito con alguna relevancia!
[A]: es


[Q]: Un muy buen artículo. Solo sugiero lo siguiente:  Las tablas en la sección de resultados deben ser explicadas.  Las tablas solo contienen el menor, mayor y media entre esos dos valores. Sería interesante considerar también la media general y la desviación estándar.  En las tablas 4, 5 y 6 solo algunas líneas contienen un valor destacado, las veces el mayor de la línea y otras el menor... Explicar mejor el significado de esas marcaciones en todas las tablas.  La referencia [12] comienza con "and" y no con el nombre del primer  autor (de echo parece que están con problemas también el resto de los autores).  La referencia [22] es la única que no usa iniciales para el nombre del autor.
[A]: es


[Q]: 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.
[A]:
en