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:
This paper addresses the network embedding problem by introducing a neural network model which uses both the network structure and associated text on the nodes, with an attention model to vary the textual representation based on the text of the neighboring nodes.  - Strengths:  The model leverages both the network and the text to construct the latent representations, and the mutual attention approach seems sensible.  A relatively thorough evaluation is provided, with multiple datasets, baselines, and evaluation tasks.  - Weaknesses:  Like many other papers in the "network embedding" literature, which use neural network techniques inspired by word embeddings to construct latent representations of nodes in a network, the previous line of work on statistical/probabilistic modeling of networks is ignored.  In particular, all "network embedding" papers need to start citing, and comparing to, the work on the latent space model of Peter Hoff et al., and subsequent papers in both statistical and probabilistic machine learning publication venues:  P.D. Hoff, A.E. Raftery, and M.S. Handcock. Latent space approaches to social network analysis. J. Amer. Statist. Assoc., 97(460):1090–1098, 2002.  This latent space network model, which embeds each node into a low-dimensional latent space, was written as far back as 2002, and so it far pre-dates neural network-based network embeddings.  Given that the aim of this paper is to model differing representations of social network actors' different roles, it should really cite and compare to the mixed membership stochastic blockmodel (MMSB):  Airoldi, E. M., Blei, D. M., Fienberg, S. E., & Xing, E. P. (2008). Mixed membership stochastic blockmodels. Journal of Machine Learning Research.  The MMSB allows each node to randomly select a different "role" when deciding whether to form each edge.  - General Discussion:  The aforementioned statistical models do not leverage text, and they do not use scalable neural network implementations based on negative sampling, but they are based on well-principled generative models instead of heuristic neural network objective functions and algorithms.  There are more recent extensions of these models and inference algorithms which are more scalable, and which do leverage text.  Is the difference in performance between CENE and CANE in Figure 3 statistically insignificant? (A related question: were the experiments repeated more than once with random train/test splits?)  Were the grid searches for hyperparameter values, mentioned in Section 5.3, performed with evaluation on the test set (which would be problematic), or on a validation set, or on the training set?
answer:
en


question:
Here is a summary of the reviews:    Strengths  Experiments are done on state-of-the-art networks, on a real speech recognition problem (R3, R1)  Networks themselves are of a very large size (R3)  Computational gains are substantial (R3, R4)  Paper is clear (R1)    Weaknesses  Experiments are all done on a private dataset (R3)  No comparison to other pruning approaches (e.g. Han et al.) (R3); AC notes that reviewers added new results which compare to an existing pruning method  No comparison to distillation techniques (R1)  Paper doesn't present much novelty in terms of ideas (R3)    The AC encouraged feedback from the reviewers following author rebuttal and paper improvements. Reviewers stated that the improvements made to the paper made it publishable but was still closer to the threshold. R1 who had originally rated the paper 3: a "clear reject" updated the score to 6 (just above acceptance).    Considering the reviews and discussions, the AC thinks that this paper is a poster accept. There are no serious flaws, the improvements made to the paper during the discussion paper have satisfied the reviewers, and this is an important topic with practical benefits; evaluated on a real large-scale problem.
answer:
en


question:
RESUMEN. El trabajo describe los pasos a seguir para aplicar conceptos de lógica difusa en dispositivos móviles. Para ejemplificar los pasos a seguir, el autor usó un robot móvil llamado AMADEUS (similar a los robots limpiadores comercializados en Japón [1]). Para aplicar los conceptos de lógica difusa, se usó módulo Mamdani.  Evaluación General. El tema es suma interesante y muy recurrente últimamente. Dispositivos que usen lógica difusa para operar es puente muy interesante y novedoso entre Inteligencia Artificial y "pervasive computing". Sin embargo, este trabajo tiene al menos una debilidad importante que debería ser resuelta:  1) En el trabajo no muestra porque es realmente importante usar lógica difusa. Un ejemplo de aplicación es fuertemente recomendable para el trabajo ¿En que ayudaría usar lógica difusa en dispositivos móviles?  2) En el trabajo no me queda muy claro si el uso de lógica difusa en dispositivos móviles es algo nuevo en esta área. Si no es algo nuevo, tal vez el trabajo podría llegar convertirse solamente en una repetición de otro trabajo. Si es algo nuevo, por favor, mencionarlo.  Comentarios Menores: - En las conclusiones, se menciona la frase "se demostró"; se sugiere cambiar a "se mostró". - Cuidar los usos de puntuaciones (ej. "...", ".") y  el uso mayúsculas. - Al final de la introducción, se sugiere mencionar la estructura del documento.  [1] Auto Mee: www.yumeki.org/comercilizaran-pequeno-robot-limpiador-de-dispositivos-tactiles
answer:

es