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].

Input: Consider 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


Input: Consider Input: I reviewed the manuscript as of December 6th.  The authors perform a systematic investigation of various retraining methods for making a classification network robust to adversarial examples. The authors achieve lower error rates using their RAD and IAEC methods perform better then previously introduced distillation methods for retraining networks to be robust to adversarial examples. This method suggests a promising direction for building a defense for adversarial examples.  Major Comments: I find the paper to not be lacking in exposition and clarity. The paper has a laundry list of related results (page 2) but no clear message. I *think* one larger point is the superior performance of their retraining techniques but it is not clear how well these techniques perform compared to other retraining techniques, nor are the details of the retraining techniques clear. The paper requires more discussion and a clear exposition about the methods the authors introduced (i.e. RAD, IAEC). What follow are some more detailed comments along this theme of improving the exposition and clarity:  - The authors should provide more details about how they constructed the auto-encoder in the IAEC method (diagram?). The same needs to be said for the RAD method. The authors point to a previous workshop submission (

Output: en


Input: Consider Input: El trabajo compara cuatro arquitecturas de navegación autónoma para robots móviles, las arquitecturas reactivo puro, redes neuronales, lógica difusa y una arquitectura hibrida neuro-difusa.  EL trabajo está bien planteado, el trabajo experimental parece replicable y los resultados presentados suenan coherenten.  El principal aspecto a mejorar sería el análisi/discusion de los resultados, el cual se puede desarrollar más.


Output: es


