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: Se explica en forma ordenada y didactica una experiencia de uso de TIC para la colaboracion academica. La originalidad del trabajo no es su dimension fuerte, ver trabajos de larga data como Gorton et al. (1997) y actuales como Lanunile et al. (2010). Sin embargo, la relevancia del tema como patron de referencia en actividades relacionadas a tesis cientificas, asociada a su buena presentacion, hacen de el un aporte. Se sugiere, en miras a una publicacion en revista, que se compare la eficiencia y efectividad de las actividades sin el apoyo de estas herramientas versus la actual realidad (con herramientas). Ejemplos de preguntas posibles a responder de este analisis exploratorio: Cuales son las principales actividades donde el uso de herramientas colaborativas es clave? Que cambios en el diseno de estas herramientas se deberian desarrollar en el futuro? Existen perfiles de estudiantes/academicos mas propicios para la adopcion de estas plataformas? Referencias: Gorton, I., Hawryszkiewycz, I., and Ragoonaden, K. 1997. Collaborative tools and processes to support software engineering shift work. BT Technology Journal 15, 3 (Jul. 1997), 189-198. Filippo Lanubile, Christof Ebert, Rafael Prikladnicki, Aurora Vizcaino, "Collaboration Tools for Global Software Engineering," IEEE Software, vol. 27, no. 2, pp. 52-55, Mar./Apr. 2010.

Answer: es

Question: - Strengths: This paper presents a 2 x 2 x 3 x 10 array of accuracy results based on systematically changing the parameters of embeddings models: (context type, position sensitive, embedding model, task), accuracy - context type {Linear, Syntactic} - position sensitive {True, False} - embedding model {Skip Gram, BOW, GLOVE} - task {Word Similarity, Analogies, POS, NER, Chunking, 5 text classific. tasks} The aim of these experiments was to investigate the variation in performance as these parameters are changed. The goal of the study itself is interesting for the ACL community and similar papers have appeared before as workshop papers and have been well cited, such as Nayak et al.'s paper mentioned below. - Weaknesses: Since this paper essentially presents the effect of systematically changing the context types and position sensitivity, I will focus on the execution of the investigation and the analysis of the results, which I am afraid is not satisfactory. A) The lack of hyper-parameter tuning is worrisome. E.g. - 395 Unless otherwise notes, the number of word embedding dimension is set to 500. - 232 It still enlarges the context vocabulary about 5 times in practice. - 385 Most hyper-parameters are the same as Levy et al' best configuration. This is worrisome because lack of hyperparameter tuning makes it difficult to make statements like method A is better than method B. E.g. bound methods may perform better with a lower dimensionality than unbound models, since their effective context vocabulary size is larger. B) The paper sometimes presents strange explanations for its results. E.g. - 115 "Experimental results suggest that although it's hard to find any universal insight, the characteristics of different contexts on different models are concluded according to specific tasks." What does this sentence even mean? - 580 Sequence labeling tasks tend to classify words with the same syntax to the same category. The ignorance of syntax for word embeddings which are learned by bound representation becomes beneficial. These two sentences are contradictory, if a sequence labeling task classified words with "same syntax" to same category then syntx becomes a ver valuable feature. Bound representation's ignorance of syntax should cause a drop in performance just like other tasks which does not happen. C) It is not enough to merely mention Lai et. al. 2016 who have also done a systematic study of the word embeddings, and similarly the paper "Evaluating Word Embeddings Using a Representative Suite of Practical Tasks", Nayak, Angeli, Manning. appeared at the repeval workshop at ACL 2016. should have been cited. I understand that the focus of Nayak et al's paper is not exactly the same as this paper, however they provide recommendations about hyperparameter tuning and experiment design and even provide a web interface for automatically running tagging experiments using neural networks instead of the "simple linear classifiers" used in the current paper. D) The paper uses a neural BOW words classifier for the text classification tasks but a simple linear classifier for the sequence labeling tasks. What is the justification for this choice of classifiers? Why not use a simple neural classifier for the tagging tasks as well? I raise this point, since the tagging task seems to be the only task where bound representations are consistently beating the unbound representations, which makes this task the odd one out. - General Discussion: Finally, I will make one speculative suggestion to the authors regarding the analysis of the data. As I said earlier, this paper's main contribution is an analysis of the following table. (context type, position sensitive, embedding model, task, accuracy) So essentially there are 120 accuracy values that we want to explain in terms of the aspects of the model. It may be beneficial to perform factor analysis or some other pattern mining technique on this 120 sample data.

Answer: en

Question: En la practica realiza una comunicacion utilizando un esquema de distribucion de procesos y datos no declarados, para lograr solucionar el problema de ruido, no indica el metodo de procesamiento de imagen, tampoco la estrategia utilizada, hace un manejo de informacion a modo de introduccion al tema CPU-GPU. Claramente es un trabajo que ataca un procesamiento de imagen preliminar, simple, donde debe mostrarse el metodo a paralizar y la estrategia a utilizar para su paralelizacion en GPU o en CPU-GPU. Desde el punto de vista de procesamiento de imagenes es un tema poco original, pero es interesante que se comience a trabajar en paralelismo en CPU-GPU's,lo que no es novedad, pero lo interesante son las estrategias de paralelizacion, el como particionar los procesos y datos, el como utilizar las grillas, bloques e hilos, el como armar una topologia si es posible, para dar una solucion que minimice el tiempo de ejecucion. Especificar claramente que es un trabajo de procesamiento de imagenes, filtros que se utiliza, metodo y estrategia de paralelizacion.

Answer:
es