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].

Resumen: Este artículo analiza un conjunto de estudios con el objetivo de conocer que piensan los investigadores sobre la técnica de educación más adecuada.  Evaluación: El trabajo tiene una estructura de un paper y la propuesta se ve interesante. Sin embargo,  - Aunque el trabajo discute las consecuencias de los resultados, (creo que) no discute claramente las potenciales causas. Tal vez, se pueden obtener conclusiones interesantes de por qué existe esta divergencia mencionada.  - No me queda claro de por qué se quiere tener una convergencia absoluta sobre estas técnicas. A una simple vista, me da la impresión que un conjunto técnicas aplicadas a cierto contexto parece ser apropiado.  - Background es demasiado corto para introducir los conceptos necesarios para comprender la propuesta y  Related work es sumamente pobre (hay solamente 1 cita)  Detalles menores:  1) El nombre de la sección 2 “background & …” es sumamente ambiguo, lo cual se vuelve difícil de saber de qué se trata la sección. Sugiero ser más específico (ej. “systematic review for elicitation techniques”?) 2) No se explica “systematic mapping”, solamente se explica su uso. 3) ¿Por qué no se habla de meta-analisis? Meta-analisis pareciera que puede contribuir a esta discusión. 4) Reemplazaría “authors” por “we” cuando amerita (ej. párrafo 2 - página 2). En ocasiones, cuesta saber si hablan de autores de un trabajo en particular o del trabajo que están presentando. 5) Reemplazaría “you” por “one”. “You” es informal. 6) “El **estudio** identificó 42 **estudios**” —> demasiado “estudio” ??? 7) Texto de “Figure 3” está separado de su imagen. 8) Hay una página en blanco en el medio del documento. 9) “In order to move in this direction, this study aims auscultating how researchers represent the appropriateness of the techniques.” ¿Cuál dirección? Es ambiguo está oración.
es

El documento describe un trabajo exploratorio en el desarrollo de una aplicación software en el área de informática médica, concretamente en los sistemas HIS (Hospital Information System). La propuesta es interesante pero el documento denota que es un trabajo insipiente, o se entiende esto, dado que se describe la arquitectura propuesta y la metodología del proyecto, pero no se describe un escenario de prueba o un caso concreto de validación. Igualmente, no se observa en el documento la descripción de un software resultado del proyecto, y por el título del artículo y el abstract se espera la descripción de alguna propuesta software.
es

This work showed that word representation learning can benefit from sememes when used in an appropriate attention scheme. Authors hypothesized that sememes can act as an essential regularizer for WRL and WSI tasks and proposed SE-WL model which detects word senses and learn representations simultaneously. Though experimental results indicate that WRL benefits, exact gains for WSI are unclear since a qualitative case study of a couple of examples has only been done. Overall, paper is well-written and well-structured.  In the last paragraph of introduction section, authors tried to tell three contributions of this work. (1) and (2) are more of novelties of the work rather than contributions. I see the main contribution of the work to be the results which show that we can learn better word representations (unsure about WSI) by modeling sememe information than other competitive baselines. (3) is neither a contribution nor a novelty.  The three strategies tried for SE-WRL modeling makes sense and can be intuitively ranked in terms of how well they will work. Authors did a good job explaining that and experimental results supported the intuition but the reviewer also sees MST as a fourth strategy rather than a baseline inspired by Chen et al. 2014 (many WSI systems assume one sense per word given a context). MST many times performed better than SSA and SAC. Unless authors missed to clarify otherwise, MST seems to be exactly like SAT with a difference that target word is represented by the most probable sense rather than taking an attention weighted average over all its senses. MST is still an attention based scheme where sense with maximum attention weight is chosen though it has not been clearly mentioned if target word is represented by chosen sense embedding or some function of it.  Authors did not explain the selection of datasets for training and evaluation tasks. Reference page to Sogou-T text corpus did not help as reviewer does not know Chinese language. It was unclear which exact dataset was used as there are several datasets mentioned on that page. Why two word similarity datasets were used and how they are different  (like does one has more rare words than another) since different models performed differently on these datasets. The choice of these datasets did not allow evaluating against results of other works which makes the reviewer wonder about next question.  Are proposed SAT model results state of the art for Chinese word similarity?  E.g. Schnabel et al. (2015) report a score of 0.640 on WordSim-353 data by using CBOW word embeddings.  Reviewer needs clarification on some model parameters like vocabulary sizes for words (Does Sogou-T contains 2.7 billion unique words) and word senses (how many word types from HowNet). Because of the notation used it is not clear if embeddings for senses and sememes for different words were shared. Reviewer hopes that is the case but then why 200 dimensional embeddings were used for only 1889 sememes. It would be better if complexity of model parameters can also be discussed.  May be due to lack of space but experiment results discussion lack insight into observations other than SAT performing the best. Also, authors claimed that words with lower frequency were learned better with sememes without evaluating on a rare words dataset.  I have read author's response.

en