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]: - Strengths:  This paper tackles an interesting problem and provides a (to my knowledge) novel and reasonable way of learning and combining cognitive features with textual features for sentiment analysis and irony detection. The paper is  clearly written and organized, and the authors provided a lot of useful detail and informative example and plots. Most of the results are convincing, and the authors did a good job comparing their approach and results with previous work.  - Weaknesses:  1. Just from the reading abstract, I expected that the authors' approach would significantly outperform previous methods, and that using both the eye-gaze and textual features consistently yields the best results. Upon reading the actual results section, however, it seems like the findings were more mixed. I think it would be helpful to update the abstract and introduction to reflect this.  2. When evaluating the model on dataset 1 for sentiment analysis, were the sarcastic utterances included? Did the model do better on classifying the non-sarcastic utterances than the sarcastic ones? 3. I understand why the eye-movement data would be useful for sarcasm detection, but it wasn't as obvious to me why it would be helpful for (non-sarcastic) sentiment classification beyond the textual features.   - General Discussion:  This paper contains a lot of interesting content, and the approach seems solid and novel to me. The results were a little weaker than I had anticipated from the abstract, but I believe would still be interesting to the larger community and merits publication.
[A]: en


[Q]: The paper presents an approach for tackling the instability problem that is present in generative adversarial networks. The general idea is to allow the generator to "peek ahead" at how the discriminator will evolve its decision boundary over-time with the premise that this information should prevent the generator from collapsing to produce only samples from a single mode of the data distribution.  This is a very well written paper that clearly motivates its attack on an important open issue. The experiments are well carried out and strongly support the presented idea. The pursued approach is substantially more elegant than current existing "hacks" that are commonly used to make GANs work in practice. I however have three main issues that let me partly doubt the success of the method. If these can be resolved this paper is a clear candidate for acceptance.  1) I am not entirely convinced that the same effect cannot be obtained by the following procedure: simply train the discriminator for an extended number of K steps when updating the generator (say a number equivalent to the unrolling steps used in the current experiments) then, after the generator was updated undo the K updates to the discriminator and do 1 new update step instead. I only briefly glanced at your response to Reviewer2 which seems to imply you now tried something similar to this setup by stopping gradient flow at an appropriate point (although I think this is not exactly equivalent). 2) I tried to reproduce the simple MNIST example but using a fully connected network instead of an RNN generator without much success. Even when unrolling the discriminator for 30-40 steps the generator still engages in mode seeking behavior or does not train at all. This could either be because of a bug in my implementation or because of some peculiarities of the RNN generator or because I did not use batch normalization anywhere. If it is one of the latter two this would entail a dependence of the proposed approach on specific forms of the discriminator and generator and should be discussed. My code can be found here
[A]: en


[Q]: This paper presents a set of experiments investigating what kinds of information are captured in common unsupervised approaches to sentence representation learning. The results are non-trivial and somewhat surprising. For example, they show that it is possible to reconstruct word order from bag of words representations, and they show that LSTM sentence autoencoders encode interpretable features even for randomly permuted nonsense sentences.  Effective unsupervised sentence representation learning is an important and largely unsolved problem in NLP, and this kind of work seems like it should be straightforwardly helpful towards that end. In addition, the experimental paradigm presented here is likely more broadly applicable to a range of representation learning systems. Some of the results seem somewhat strange, but I see no major technical concerns, and think that that they are informative. I recommend acceptance.  One minor red flag:  - The massive drop in CBOW performance in Figures 1b and 4b are not explained, and seem implausible enough to warrant serious further investigation. Can you be absolutely certain that those results would appear with a different codebase and different random seed implementing the same model? Fortunately, this point is largely orthogonal to the major results of the paper.  Two writing comments: - I agree that the results with word order and CBOW are surprising, but I think it's slightly misleading to say that CBOW is predictive of word order. It doesn't represent word order at all, but it's possible to probabilistically reconstruct word order from the information that it does encode. - Saying that "LSTM auto-encoders are more effective at encoding word order than word content" doesn't really make sense. These two quantities aren't comparable.
[A]:
en