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].

Ex Input:
This paper presents a set of experiments investigating what kinds of information are captured in common unsupervised approaches to sentence representation learning. The results are non-trivial and somewhat surprising. For example, they show that it is possible to reconstruct word order from bag of words representations, and they show that LSTM sentence autoencoders encode interpretable features even for randomly permuted nonsense sentences.  Effective unsupervised sentence representation learning is an important and largely unsolved problem in NLP, and this kind of work seems like it should be straightforwardly helpful towards that end. In addition, the experimental paradigm presented here is likely more broadly applicable to a range of representation learning systems. Some of the results seem somewhat strange, but I see no major technical concerns, and think that that they are informative. I recommend acceptance.  One minor red flag:  - The massive drop in CBOW performance in Figures 1b and 4b are not explained, and seem implausible enough to warrant serious further investigation. Can you be absolutely certain that those results would appear with a different codebase and different random seed implementing the same model? Fortunately, this point is largely orthogonal to the major results of the paper.  Two writing comments: - I agree that the results with word order and CBOW are surprising, but I think it's slightly misleading to say that CBOW is predictive of word order. It doesn't represent word order at all, but it's possible to probabilistically reconstruct word order from the information that it does encode. - Saying that "LSTM auto-encoders are more effective at encoding word order than word content" doesn't really make sense. These two quantities aren't comparable.

Ex Output:
en


Ex Input:
The authors propose a method to investigate the predictiveness of intermediate layer activations. To do so, they propose training linear classifiers and evaluate the error on the test set.  The paper is well motivated and aims to shed some light onto the progress of model training and hopes to provide insights into deep learning architecture design.  The two main reasons for why the authors decided to use linear probes seem to be: - convexity - The last layer in the network is (usually) linear  In the second to last paragraph of page 4 the authors point out that it could happen that the intermediate features are useless for a linear classifier. This is correct and what I consider the main flaw of the paper. I am missing any motivation as to the usefulness of the suggested analysis to architecture design. In fact, the example with the skip connection (Figure 8) seems to suggest that skip connections shouldn't be used. Doesn't that contradict the recent successes of ResNet?  While the results are interesting, they aren't particularly surprising and I am failing to see direct applicability to understanding deep models as the authors suggest.

Ex Output:
en


Ex Input:
El artículo presenta una comparación cuantitativa (Técnica DEA) y comparación cualitativa (Metodología MESME) de modelos de madurez BI seleccionados. El artículo es una aproximación para la generación de una futura guía metodológica enfocada a implementar mejoras en la madurez en BI para una organización. Por lo tanto, es una investigación inicial, pero la comparativa realizada es interesante.  Para la comparación cuantitativa se seleccionaron 5 de las 6 metodologías. Después, cuando se hizo el análisis cualitativo se trabajó con 6 metodologías y no con 5, incluyendo la ya descartada.  En la página 2 dice "La presente investigación, tiene como objetivo identificar ...". El objetivo no es "identificar" sino "realizar una comparación para seleccionar ...".  En la sección IV se habla de una revisión sistemática, pero no pone ninguna referencia asociada a ese trabajo anteriormente realizado. ¿Cómo seleccionó los modelos y de dónde?  Otras observaciones y/o dudas: - La Referencia 18 no es citada en el texto. - Hay 4 referencias asociadas a modelos de madurez ([3], [11], [15], [17]), ¿Ninguna hace una comparativa? - Revisar el inglés en Keywords - Cambiar "Palabras claves" por "Palabras clave" - En página 2, cambiar "En la sección V detalla" por "En la sección V se detalla" o por "La sección V detalla". - El mismo problema ocurre en la página 3 con "La Figura 3 se aprecia", "La Tabla 2 se describe", "La Figura 6 se muestra", "Se observa en la Tabla 11 se contabiliza". - Debe corregir palabras "pegadas". Por ejemplo, cambiar "aanalizar", "dedetalle", etc. - No indica el significado de cada uno de los elementos de la ecuación 3. - Antes de la Tabla 7, hay texto solamente en una columna. - En página 8, Tabla 9, cambiar "para integran" por "para integrar". - Corregir palabras mal escritas: "escazas", "se precia". - En la página 9 habla de "investigación de tesis" y no de artículo. - En Referencia [7] está escrito "Jour-nal", En Referencia [13] aparece un número después del apellido de los autores (ejemplo: "Tan1", "Sin1", "Yeoh2" - En Referencia [18] aparece "2010b"

Ex Output:

es