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].
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Question: Resumen: Este articulo analiza un conjunto de estudios con el objetivo de conocer que piensan los investigadores sobre la tecnica de educacion mas adecuada. Evaluacion: 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 que existe esta divergencia mencionada. - No me queda claro de por que se quiere tener una convergencia absoluta sobre estas tecnicas. A una simple vista, me da la impresion que un conjunto tecnicas 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 seccion 2 "background & " es sumamente ambiguo, lo cual se vuelve dificil de saber de que se trata la seccion. Sugiero ser mas especifico (ej. "systematic review for elicitation techniques"?) 2) No se explica "systematic mapping", solamente se explica su uso. 3) Por que no se habla de meta-analisis? Meta-analisis pareciera que puede contribuir a esta discusion. 4) Reemplazaria "authors" por "we" cuando amerita (ej. parrafo 2 - pagina 2). En ocasiones, cuesta saber si hablan de autores de un trabajo en particular o del trabajo que estan presentando. 5) Reemplazaria "you" por "one". "You" es informal. 6) "El **estudio** identifico 42 **estudios**" > demasiado "estudio" ??? 7) Texto de "Figure 3" esta separado de su imagen. 8) Hay una pagina 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." Cual direccion? Es ambiguo esta oracion.

Answer: es

Question: This paper studies neural models that can be applied to set-structured inputs and thus require permutation invariance or equivariance. After a first section that introduces necessary and sufficient conditions for permutation invariance/equivariance, the authors present experiments in supervised and semi-supervised learning on point-cloud data as well as cosmology data. The reviewers agreed that this is a very promising line of work and acknowledged the effort of the authors to improve their paper after the initial discussion phase. However, they also agree that the work appears to be missing more convincing numerical experiments and insights on the choice of neural architectures in the class of permutation-covariant. In light of these reviews, the AC invites their work to the workshop track. Also, I would like to emphasize an aspect of this work that I think should be addressed in the subsequent revision. As the authors rightfully show (thm 2.1), permutation equivariance puts very strong constraints in the class of 1-layer networks. This theorem, while rigorous, reflects a simple algebraic property of matrices that commute with permutation matrices. It is therefore not very surprising, and the resulting architecture relatively obvious. So much so that it already exists in the literature. In fact, it is a particular instance of the graph neural network model of Scarselli et al. '09 (

Answer: en

Question: The paper proposed to analyze several recently developed machine readers and found that some machine readers could potentially take advantages of the entity marker (given that the same marker points out to the same entity). I usually like analysis papers, but I found the argument proposed in this paper not very clear. I like the experiments on the Stanford reader, which shows that the entity marker in fact helps the Stanford reader on WDW. I found that results rather interesting. However, I found the organization and the overall message of this paper quite confusing. First of all, it feels that the authors want to explain the above behavior with some definition of the "structures". However, I am not sure that how successful the attempt is. For me, it is still not clear what the structures are. This makes reading section 4 a bit frustrating. I am also not sure what is the take home message of this paper. Does it mean that the entity marking should be used in the MR models? Should we design models that can also model the entity reference at the same time? What are the roles of the linguistic features here? Should we use linguistic structure to overcome the reference issue? Overall, I feel that the analysis is interesting, but I feel that the paper can benefit from having a more focused argument.

Answer:
en