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 reviewers seem to agree that the framework presented is not very novel, something I agree with.  The experiments show that the low rank + diagonal parameterization can be useful, however. The paper could be improved by making a more tightened message, and clearer arguments. As it currently stands, however it does not seem ready for publication in ICLR.

Ex Output:
en


Ex Input:
El artículo presenta una evaluación de técnicas de algoritmos genéticos para la ingeniería inversa en línea de productos de software. La mayor parte del trabajo se enfoca en presentar y explicar detalladamente la Ingeniería de Líneas de Productos de Software (ILPS) y la aplicación de Ingeniería inversa sobre este paradigma. La explicación asociada a ILPS y a Ingeniería Reversa es detallada en la Introducción del artículo, incluso para ingeniería reversa se entregan más dos definiciones para el mismo concepto. Sin embargo, en la misma sección no se hace referencia alguna a Algoritmos Genéticos que también es parte de este trabajo.  En general el trabajo es interesante pero carece de un alto impacto innovador respecto a trabajos asociados a Algoritmos Genéticos.  En la sección de Resultado y Evaluación se muestra un detalle de Tablas de Resultados, sin embargo la discusión y explicación de resultados solo se entrega en 1 párrafo. Se esperaría una explicación más clara respecto al significado de cada una de las columnas de la tabla y resultados correspondientes.  En la sección de referencias hay 3 referencias [11], [12], [13] para establecer una definición, se esperaría tener mayor referencias a trabajos similares, quizás que se pudiera incluir una sección de Related Work.  Menores. -- En general se recomienda revisar la redacción de algunas frases. Solo como ejemplo recomiendo revisar la última frase del Resumen. Otro ejemplo es el párrafo de AGRADECIMIENTOS. -- En figura 2 no se visualiza correctamente la serie "Con LPS" en impresión Blanco/Negro -- En pág. 8 penúltimo párrafo hay un "." que es del párrafo anterior en una línea en blanco. -- La tabla 2 tiene un formato de Fuente más grande y al parecer de otro tipo al del resto del artículo.

Ex Output:
es


Ex Input:
In this paper authors propose a novel data augmentation scheme where instead of augmenting the input data, they augment intermediate feature representations.  Sequence auto-encoder based features are considered, and random perturbation, feature interpolation, and extrapolation based augmentation are evaluated. On three sequence classification tasks and on MNIST and CIFAR-10, it is shown that augmentation in feature space, specifically extrapolation based augmentation, results in good accuracy gains w.r.t. authors baseline.  My main questions and suggestions for further strengthening the paper are:  a) The proposed data augmentation approach is applied to a learnt auto-encoder based feature space termed ‘context vector’ in the paper.  The context vectors are then augmented and used as input to train classification models. Have the authors considered applying their feature space augmentation idea directly to the classification model during training, and applying it to potentially many layers of the model?  Also, have the authors considered convolutional neural network (CNN) architectures as well for feature space augmentation?  CNNs are now the state-of-the-art in many image and sequence classification task, it would be very valuable to see the impact of the proposed approach in that model.  b) When interpolation or extrapolation based augmentation was being applied, did the authors also consider utilizing nearby samples from competing classes as well?  Especially in case of extrapolation based augmentation it will be interesting to check if the extrapolated features are closer to competing classes than original ones.  c) With random interpolation or nearest neighbor interpolation based augmentation the accuracy seems to degrade pretty consistently.  This is counter-intuitive.  Do the authors have explanation for why the accuracy degraded with interpolation based augmentation?  d) The results on MNIST and CIFAR-10 are inconclusive.  For instance the error rate on CIFAR-10 is well below 10% these days, so I think it is hard to draw conclusions based on error rates above 30%.  For MNIST it is surprising to see that data augmentation in the input space substantially degrades the accuracy (1.093% -> 1.477%).  As mentioned above, I think this will require extending the feature space augmentation idea to CNN based models.

Ex Output:

en