instruction:
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].
question:
El trabajo presenta un estudio del estado del arte en lo que respecta a sistemas de robótica cooperativa de multi-robots. El foco de la revisión es la arquitectura de control presente en aquellos sistemas.  El abstract en inglés debe ser revisado en su redacción, en especial el uso de palabras, como "giving"->"providing", "panoramic"->"general", etc.  La introducción no entrega un mayor aporte a la estructura del documento. Se habla un poco de la investigación asociada al tema, pero con un uso muy bajo en referencias y la sección termina abruptamente sin explicar metodología o contribuciones del trabajo.  El trabajo entrega un "planteamiento del problema" muy poco claro y que no provee mayor relevancia. Más aun, además de identificar algunas aproximaciones al contexto, el trabajo no plantea ninguna solución, alternativa, categorización, marco de trabajo o aporte en general al (poco claro) problema presentado.  En resumen, el trabajo realiza una recopilación relativamente adecuada de información respecto a sistemas de robótica cooperativa de multi-robots, sin embargo, más allá de eso, no se revela un real aporte o relevancia desde el punto de vista científico al trabajo. Si estos puntos fueran más claros, si se presentara de forma directa la metodología, y si se presentara un producto concreto de investigación, este trabajo podría ser aceptado, pero en su estado actual no se recomienda.
answer:
es


question:
Interesting application domain.  Nothing new from a machine learning perspective. They authors should provide more information about the models they have obtained with the different classifiers (feature subset selection used, selected features, accuracies, statistical significance on the differences on accuracies, the models themselves, ...). Semi-supervised classification is the appropriate method for the last paragraph in the "Methodology" section.
answer:
en


question:
This paper describes a system to assist written test scoring.  - Strengths: The paper represents an application of an interesting NLP problem -- recognizing textual entailment -- to an important task -- written test scoring.  - Weaknesses: There isn't anything novel in the paper. It consist of an application of an existing technology to a known problem.  The approach described in the paper is not autonomous -- it still needs a human to do the actual scoring. The paper lacks any quantitative or qualitative evaluation of how useful such system is. That is, is it making the job of the scorer easier? Is the scorer more effective as compared to not having automatic score?  The system contains multiple components and it is unclear how the quality of each one of them contributes to the overall experience.  The paper needs more work with the writing. Language and style is rough in several places.  The paper also contains several detailed examples, which don't necessarily add a lot of value to the discussion.   For the evaluation of classification, what is the baseline of predicting the most frequent class?  - General Discussion: I find this paper not very inspiring. I don't see the message in the paper apart from announcing having build such a system
answer:

en