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].

Ex Input:
A pesar de que el tema referente a la problemática de la navegación autónoma de robots móviles es de interés, el trabajo presentado no necesariamente tendría que serlo. Sin embargo a medida que el lector aborda en profundidad el documento es posible darse cuenta que el trabajo presentado constituye un aporte al tema. Ya desde el título se plantea con claridad lo que el lector encontrará en el documento, lo cual se ve reforzado con un planteamiento sencillo del tema por parte del autor facilitando la comprensión del trabajo realizado. Por otra parte, se presenta un prototipo cuya implementación es posible de replicar en laboratorio por quien se sienta interesado en el tema, lo cual contribuye a que este trabajo sea considerado como posible referencia en el ámbito educacional.

Ex Output:
es


Ex Input:
Overview: This work seems very promising, but I believe it should be compared with more baselines, and more precisely described and explained, from a signal processing point of view.  Pros: New descriptor Fast implementation  Cons: a) Lack of rigor b) Too long accordingly to the content c) The computational gain of the algorithm is not clear d) The work is not compared with its most obvious baseline: a scattering transform  I will detail each cons.  a) Section 1: The author  motivates the use of scattering transform because it defines a contraction of the space that relies on geometric features. " The nonlinearity used in the scattering network is the complex modulus which is piecewise linear." A real modulus is piecewise linear. A complex modulus has a shape of bell when interpreting C as R^2. Could you clarify? \Omega is not introduced.  Could you give a precise reference (page+paper) of this claim: “Higher order nonlinearity refers to |x|^2 instead of |x| as it is usually done in the scattering network.” ?  Section 2: The motivation of the non-linearity is not clear. First, this non-linearity might potentially increase a lot the variance of your architecture since it depends on higher moments(up to 4). I think a fair analysis would be to compute numerically the normalized variance (e.g. divided by the averaged l^2 norm), as a sanity check. Besides, one should prove that the energy is decreasing. It is not possible to argue that this architecture is similar to a scattering transform which has precise mathematical foundations and those results are required, since the setting is different.  Permutation is not a relevant variability.  The notion of sparsity during the whole paper sometimes refers to the number of 0 value, either the l^1 norm. Mathematically, a small value, even 10^-1000 is still a non 0 value.  Did you compute the graph of the figure 4 on the bird dataset? You might use a ratio instead for clarity.   The wavelet that is defined is not a morlet wavelet (

Ex Output:
en


Ex Input:
This paper presents a method for embedding data instances into a low-dimensional space that preserves some form of similarity.  Although the paper presents this notion as new, basically every pre-trained embedding (be it auto-encoders or word2vec) has been doing the same: representing items in a low-dimensional space that inherently encodes their similarities. Even when looking at the specific case of word/context embeddings, the method is not novel either: this method is almost identical to one of the similarity functions presented in "A Simple Word Embedding Model for Lexical Substitution" (Melamud et al., 2015). The novelty claim must be more accurate and position itself with respect to existing work.  In addition, I think the evaluation could be done better. There are plenty of benchmarks for word embeddings in context, for example:  *

Ex Output:

en