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].

[Q]: The authors propose a simple idea. They penalize confident predictions by using the entropy of the predictive distribution as a regularizer. The authors consider two variations on this idea. In one, they penalize the divergence from the uniform distribution. In the other variation, they penalize distance from the base rates. They term this variation "unigram" but I find the name odd as I've never seen multi-class labels described as unigrams before. What would a bigram be? The idea is simple, and while it's been used in the context of reinforcement learning, it hasn't been popularized as a regularizer for improving generalization in supervised learning. The justifications for the idea still lacks analysis. And the author responses comparing it to L2 regularization have some holes. A simple number line example with polynomial regression makes clear how L2 regularization could prevent a model from badly overfitting to accommodate every data point. In contrast, it seems trivial to fit every data point and satisfy arbitrarily high entropy. Of course, the un-regularized optimization is to maximize log likelihood, not simply to maximize accuracy. And perhaps something interesting may be happening at the interplay between the log likelihood objective and the regularization objective. But the paper doesn't indicate precisely what. I could imagine the following scenario: when the network outputs probabilities near 0, it can get high loss (if the label is 1). The entropy regularization could be stabilizing the gradient, preventing sharp loss on outlier examples. The regularization then might owe mainly to faster convergence. Could the authors analyze the effect empirically, on the distribution of the gradient norms? The strength of this paper is its empirical rigor. The authors take their idea and put it through its paces on a host of popular and classic benchmarks spanning CNNs and RNNs. It appears that on some datasets, especially language modeling, the confidence penalty outperforms label smoothing. At present, I rate this paper as a borderline contribution but I'm open to revising my review pending further modifications. Typo: In related work: "Penalizing entropy" - you mean penalizing low entropy
[A]: en

[Q]: Update: After reading the rebuttal comments and the revised paper, I'm leaving the rating as it was before. This paper proposes an unsupervised algorithm for transferring samples from one domain to another (related) domain under the constraint that some predefined f returns same result for the input and the result. Pros: 1. The paper presents an interesting idea of comparing samples from different domains using a fixed perceptual function f. 2. The proposed method produces visually appealing results on several datasets 3. The authors demonstrate how their approach can be used for domain adaptation and obtain improved results on the SVHN->MNIST task 4. The paper is well-written and easy to read Cons: 1. The novelty of the method is relatively minor (I consider f-constancy term as the main contribution) 2. It feels like the proposed approach would break for more dissimilar domains. The method relies on a fixed f which is trained on the source domain. This f can potentially drop information important for obtaining 1) better reconstructions in the target domain 2) more tightly related x and g(f(x)). I think the authors should consider either training all the modules in the model end-to-end or incorporating target samples into the training of f. 3. A single domain adaptation experiment is definitely not enough to consider the proposed method as a universal alternative to the existing DA approaches. I would also like to point out that using super-resolved outputs as opposed to the actual model's outputs can produce a false impression of the visual quality of the transferred samples. I'd suggest moving original outputs from the appendix into the main part.
[A]: en

[Q]: Es un excelente trabajo de investigacion de aplicacion tecnologica, vinculada con una empresa de la zona, articulo muy apropiado para Infonor Tiene fallas con respecto a un articulo tradicional, no hay trabajos relacionados, citaciones, referencias, etc. Tiene problemas de redaccion, pero es claro en lo que presenta. Tiene frases largas y hay problemas de puntuacion. Habria que adaptarlo para que pase la revision de la revista.
[A]:
es