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:
The authors proposed to use leaky rectified linear units replacing binary units in Gaussian RBM.  A sampling method was presented to train the leaky-ReLU RBM. In the experimental section, AIS estimated likelihood on Cifar10 and SVHN were reported.   It's interesting for trying different nonlinear hidden units for RBM. However, there are some concerns for the current work.  1. The author did not explain why the proposed sampling method (Alg. 2) is correct. And the additional computation cost (the inner loop and the projection) should be discussed.  2. The results (both the resulting likelihood and the generative samples) of Gaussian RBM are much worse than what we have experienced. It seems that the Gaussian RBM were not trained properly.  3. The representation learned from a good generative model often helps the classification task when there are fewer label samples. Gaussian RBM works well for texture synthesis tasks in which mixing is an important issue. The authors are encouraged to do more experiments in these two direction.

Ex Output:
en


Ex Input:
Me parece un artículo interesante de ser considerado, aunque el objetivo final del mismo es algo oscuro: "medir la posición de robot en un campo de futbol". En particular, no es claro si el trabajo se orienta a un objetivo posterior más ambicioso (múltiples robots, movimiento del equipo en tiempo real, y que hacer finalmente con estos datos???).  Por lo que puede deducirse, aunque no es explícitamente dicho, se hicieron mediciones de "un" robot "ubicándolo" en diferentes posiciones del campo, pero no con este moviéndose en tiempo real. Se presentan así resultados, que podrían haberse obtenido con otra configuración más clásica del área de visión computacional (el aporte del robot y el campo de futbol, al menos hasta ahora no tienen una relevancia fundamental en el trabajo desarrollado).  Finalmente, no me parece que los resultados obtenidos sean estrictamente novedosos, pero aun así puede ser una experiencia atractiva de ser conocida.

Ex Output:
es


Ex Input:
This work proposes to compute embeddings of symbolic expressions (e.g., boolean expressions, or polynomials) such that semantically equivalent expressions are near each other in the embedded space.  The proposed model uses recursive neural networks where the architecture is made to match that of the parse tree of a given symbolic expression.  To train the model parameters, the authors create a dataset of expressions where semantic equivalence relationships are known and minimize a loss function so that equivalent expressions are closer to each other than non-equivalent expressions via a max-margin loss function.  The authors also use a “subexpression forcing” mechanism which, if I understand it correctly, encourages the embeddings to respect some kind of compositionality.  Results are shown on a few symbolic expression datasets created by the authors and the proposed method is demonstrated to outperform baselines pretty convincingly.  I especially like the PCA visualization where the action of negating an expression is shown to correspond roughly to negating the embedding in its vector space — it is a lot like the man - woman + queen = king type embeddings that we see in the word2vec and glove style papers.    The weakest part of the paper is probably that the setting seems somewhat contrived — I can’t really think of a real setting where it is easy to have a training set of known semantic equivalences, but still more worth it to use a neural network to do predictions.   The authors have also punted on dealing with variable names, assuming that distinct variables refer to different entities in the domain.  This is understandable, as variable names add a whole new layer of complexity on an already difficult problem, but also seems high limiting.  For example, the proposed methods would not be useable in an “equation search engine” unless we were able to accurately canonicalize variable names in some way.  Other miscellaneous points: * Regarding problem hardness, I believe that the problem of determining if two expressions are equivalent is actually undecidable — see the “word problem for Thue systems”.  Related to this, I was not able to figure out how the authors determine ground truth equivalence in their training sets.  They say that expressions are simplified into a canonical form and grouped, but this seems to not be possible in general, so one question is — is it possible that equivalent expressions in the training data would have been mapped to different canonical forms?  Would it have been easier/possible to construct and compare truth tables? * The “COMBINE” operation uses what the authors describe as a residual-like connection.  Looking at the equations, the reason why this is not actually a residual connection is because of the weight matrix that is multiplied by the lower level l_0 features.  A true residual connection would have passed the features through unchanged (identity connection) and would have also been better at fighting gradient explosion…. so is there a reason why this was used rather than an identity connection? * In table 3, the first tf-idf entry: a + (c+a) * c seems equivalent to a + (c * (a+c)) * Vertical spacing between Figure 4 caption and body of text is very small and looks like the caption continues into the body of the text.

Ex Output:

en