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].
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Question: In this paper, the authors propose a new method to learn hierarchical representations of sentences, based on reinforcement learning. They propose to learn a neural shift-reduce parser, such that the induced tree structures lead to good performance on a downstream task. They use reinforcement learning (more specifically, the policy gradient method REINFORCE) to learn their model. The reward of the algorithm is the evaluation metric of the downstream task. The authors compare two settings, (1) no structure information is given (hence, the only supervision comes from the downstream task) and (2) actions from an external parser is used as supervision to train the policy network, in addition to the supervision from the downstream task. The proposed approach is evaluated on four tasks: sentiment analysis, semantic relatedness, textual entailment and sentence generation. I like the idea of learning tree representations of text which are useful for a downstream task. The paper is clear and well written. However, I am not convinced by the experimental results presented in the paper. Indeed, on most tasks, the proposed model is far from state-of-the-art models: - sentiment analysis, 86.5 v.s. 89.7 (accuracy); - semantic relatedness, 0.32 v.s. 0.25 (MSE); - textual entailment, 80.5 v.s. 84.6 (accuracy). From the results presented in the paper, it is hard to know if these results are due to the model, or because of the reinforcement learning algorithm. PROS: - interesting idea: learning structures of sentences adapted for a downstream task. - well written paper. CONS: - weak experimental results (do not really support the claim of the authors). Minor comments: In the second paragraph of the introduction, one might argue that bag-of-words is also a predominant approach to represent sentences. Paragraph titles (e.g. in section 3.2) should have a period at the end. ---------------------------------------------------------------------------------------------------------------------- UPDATE I am still not convinced by the results presented in the paper, and in particular by the fact that one must combine the words in a different way than left-to-right to obtain state of the art results. However, I do agree that this is an interesting research direction, and that the results presented in the paper are promising. I am thus updating my score from 5 to 6.

Answer: en

Question: 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.

Answer: en

Question: El campo de utilizacion de la aplicacion es muy buena y necesaria, por lo que contribuye a la robotica. En la utilizacion de vision, falta incorporar tecnologias de vision computacional mas actuales que la deteccion de objetos por flujo optico, tal vez en este caso sea mas factible utilizar tecnologia de dos vistas con imagenes sucesivas. Su contenido es bastante claro. Debe mejorar los siguiente: Pagina 1 Resumen y titulo : Si bien esta en ingles, falta escribir el resumen y el titulo en espanol o portugues Pagina 2 No presenta trabajos relacionados, Si bien presenta referencias, no hace una presentacion de trabajos relacionados Pagina 3 No indica fielmente como detecta las botellas plasticas u obstaculos En vez de colocar a la popular Lena, seria mas interesante colocar el como detecta las botellas plasticas u otros objetos con el detector de borde que utiliza. Pagina 3 La referencia [10] Existen trabajos de flujo optico mas actuales al indicado Pagina 6 En Resultados Falta medir el grado efectividad de la recoleccion de objetos, se deberia presentar medidas al respecto. A que velocidad funciona el limpiador .. Falta mas trabajo en este item. Pagina 6 Robustez estructural alcanzada No presenta datos que permitan aseverar esto. Pagina 7 Idem parrafo 2 No presentan la efectividad de la tecnologia de vision computacional ni como contribuye a evitar obstaculos, deteccion y recoleccion.. Es decir, se entiende que lo presentado se puede lograr, pero no presenta datos para concluir aquello.

Answer:
es