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].

The authors introduce a new memory model which allows memory access in O(log n) time.  Pros: * The paper is well written and everything is clear. * It's a new model and I'm not aware of a similar model. * It's clear that memory access time is an issue for longer sequences and it is clear how this model solves this problem.  Cons: * The motivation for O(log n) access time is to be able to use the model on very long sequences. While it is clear from the definition that the computation time is low because of its design, it is not clear that the model will really generalize well to very long sequences. * The model was also not tested on any real-world task.  I think such experiments should be added to show whether the model really works on long sequences and real-world tasks, otherwise it is not clear if this is a useful model.
en

No existen hipótesis en la investigación!  No se aprecia la contribución del trabajo  Varios problemas conceptuales en la introducción (ej. "SVM son clasificadores no-lineales"  .. son lineales.. lo que pasa que se pueden extender a problemas no separables linealmente mapeando a dimensiones mayores usando funciones de kernel!! , etc ).  No es claro el objetivo de los experimentos. Que se está tratando de probar? que se puede decir de los resultados (accuracy, etc) obtenidos? porque los resultados son diferentes (mejores/peores, etc) que los otros métodos que se mencionan?
es

This paper introduces the concept of fuzzy paraphrases to aid in the learning of distributed word representations from a corpus augmented by a lexicon or ontology. Sometimes polysemy is context-dependent, but prior approaches have neglected this fact when incorporating external paraphrase information during learning. The main idea is to introduce a function that essentially judges the context-sensitivity of paraphrase candidates, down-weighting those candidates that depend strongly on context. This function is inferred from bilingual translation agreement.  The main argumentation leading to the model selection is intuitive, and I believe that the inclusion of good paraphrases and the elimination of bad paraphrases during training should in principle improve word representation quality. However, the main questions are how well the proposed method achieves this goal, and, even if it achieves it well, whether it makes much difference in practical terms.  Regarding the first question, I am not entirely convinced that the parameterization of the control function f(x_ij) is optimal. It would have been nice to see some experiments investigating different choices, in particular some baselines where the effect of f is diminished (so that it reduces to f=1 in the limit) would have been interesting. I also feel like there would be a lot to gain from having f be a function of the nearby word embeddings, though this would obvious incur a significant slowdown. (See for example 'Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space' by Neelakantan et al, which should probably be cited.) As it stands, the experimental results do not clearly distinguish the fuzzy paraphrase approach from prior work, i.e. tables 3 and 4 do not show major trends one way or the other.  Regarding the second question, it is hard to draw many conclusions from analogy tasks alone, especially when effects unrelated to good/bad paraphrasing such as corpus size/content, window size, vocabulary size, etc., can have an outsize effect on performance.   Overall, I think this is a good paper presenting a sensible idea, but I am not convinced by the experiments that the specific approach is achieving its goal. With some improved experiments and analysis, I would wholeheartedly recommend this paper for acceptance; as it stands, I am on the fence.

en