instruction:
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].
question:
This paper proposes a novel strategy for zero-resource translation where (source, pivot) and (pivot, target) parallel corpora are available. A teacher model for p(target|pivot) is first trained on the (pivot, target) corpus, then a student model for p(target|source) is trained to minimize relative entropy with respect to the teacher on the (source, pivot) corpus. When using word-level relative entropy over samples from the teacher, this approach is shown to outperform previous variants on standard pivoting, as well as other zero-resource strategies.  This is a good contribution: a novel idea, clearly explained, and with convincing empirical support. Unlike some previous work, it makes fairly minimal assumptions about the nature of the NMT systems involved, and hence should be widely applicable.  I have only a few suggestions for further experiments. First, it would be interesting to see how robust this approach is to more dissimilar source and pivot languages, where intuitively the true p(target|source) and p(target|pivot) will be further apart. Second, given the success of introducing word-based diversity, it was surprising not to see a sentence n-best or sentence-sampling experiment. This would be more costly, but not much more so since you’re already doing beam search with the teacher. Finally, related to the previous, it might be interesting to explore transition from word-based diversity to sentence-based as the student converges and no longer needs the signal from low-probability words.  Some further comments:  line 241: Despite its simplicity -> Due to its simplicity  277: target sentence y -> target word y  442: I assume that K=1 and K=5 mean that you compare probabilities of the most probable and 5 most probable words in the current context. If so, how is the current context determined - greedily or with a beam?  Section 4.2. The comparison with an essentially uniform distribution doesn’t seem very informative here: it would be extremely surprising if p(y|z) were not significantly closer to p(y|x) than to uniform. It would be more interesting to know to what extent p(y|z) still provides a useful signal as p(y|x) gets better. This would be easy to measure by comparing p(y|z) to models for p(y|x) trained on different amounts of data or for different numbers of iterations. Another useful thing to explore in this section would be the effect of the mode approximation compared to n-best for sentence-level scores.  555: It’s odd that word beam does worse than word greedy, since word beam should be closer to word sampling. Do you have an explanation for this?  582: The claimed advantage of sent-beam here looks like it may just be noise, given the high variance of these curves.
answer:
en


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No existen hipótesis en la investigación!  No se aprecia la contribución del trabajo  Varios problemas conceptuales en la introducción (ej. "SVM son clasificadores no-lineales"  .. son lineales.. lo que pasa que se pueden extender a problemas no separables linealmente mapeando a dimensiones mayores usando funciones de kernel!! , etc ).  No es claro el objetivo de los experimentos. Que se está tratando de probar? que se puede decir de los resultados (accuracy, etc) obtenidos? porque los resultados son diferentes (mejores/peores, etc) que los otros métodos que se mencionan?
answer:
es


question:
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
answer:

en