Definition: 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].

Positive Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: es

Positive Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: en

Negative Example 1 -
Input: Este artículo no es un artículo de investigación, ya que sólo muestra cómo programar un robot mediante la herramienta de lógica difusa. Este tema ya ha sido propuesto como solución en navegación de robots.
Output: en

Negative Example 2 -
Input: I don't think the work reported in this paper is suitable for the SCCC conference. The author should consider reporting this experience on a conference more focused on practice in computer engineering than in CS reserarch. The material presented in the paper could be of interest for practicioners working with Oracle, namely a particular product.
Output: es

Now complete the following example -
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.
Output: 

 en