TASK DEFINITION: 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].
PROBLEM: This work investigates the performance of transfer learning from resource-rich setup (BookTest, CNN/Daily Mail corpora) to low-resource (bAbI, SQuAD benchmarks) settings. Experiments show poor improvements in 0-shot learning. However, when the model is exposed to few training instances some improvements are observed.  The claims made here require a more comprehensive analysis. I criticize the use of bAbI as a low-resource real-world scenario. bAbI is designed as a unit test and is far from representing many natural language phenomena. Thus, the claims related to bAbI can only be weak evidence for questioning transfer learning high-resource to low-resource in real-world scenarios. I highly recommend using recently proposed real-world scenarios [1,2].  More importantly, the work does not explain why and how do we get improvement using transfer learning. They remotely address this by hypothesizing the knowledge of transfer is not just encoded in embeddings but also in the model. Considering the related work [3], these claims bring a marginal novelty and still "how and why" should be central in this work.    [1]

SOLUTION: en

PROBLEM: From my original comments:  The results looks good but the baselines proposed are quite bad.  For instance in the table 2 "Misclassification rate for a 784-1024-1024-1024-10 " the result for the FC with floating point is 1.33%. Well far from what we can obtain from this topology, near to 0.8%. I would like to see "significant" compression levels on state of the art results or good baselines. I can get 0,6% with two FC hidden layers...  In CIFAR-10 experiments, i do not understand  why "Sparsely-Connected 90% + Single-Precision Floating-Point" is worse than "Sparsely-Connected 90% + BinaryConnect". So it is better to use binary than float.   Again i think that in the experiments the authors are not using all the techniques that can be easily applied to float but not to binary (gaussian noise or other regularizations). Therefore under my point of view the comparison between float and binary is not fair. This is a critic also for the original papers about binary and ternary precision.   In fact with this convolutional network, floating (standard) precision we can get lower that 9% of error rate. Again bad baselines.  ----  The authors reply still does not convince me.  I still think that the same technique should be applied on more challenging scenarios.

SOLUTION: en

PROBLEM: The authors extend GANs by an inference path from the data space to the latent space and a discriminator that operates on the joint latend/data space. They show that the theoretical properties of GANs still hold for BiGAN and evaluate the features learned unsupervised in the inference path with respect to performance on supervised tasks after retraining deeper layers.  I see one structural issue with this paper: Given that, as stated in the abstract, the main purpose of the paper is to learn unsupervised features (and not to improve GANs), the paper might spent too much space on detailing the relationship to GANs and all the theoretical properties. It is not clear whether they actually would help with the goal of learning good features. While reading the paper, I actually totally forgot about the unsupervised features until they reappeared on page 6. I think it would be helpful if the text of the paper would be more aligned with this main story.  Still, the BiGAN framework is an elegant and compelling extension to GANs. However, it is not obvious how much the theoretical properties help us as the model is clearly not fully converged. To me, especially Figure 4 seems to suggest that G(E(x)) might be doing not much more than some kind of nearest neighbour retrival (and indeed one criticism for GANs has always been that they might just memorize some samples). By the way, it would be very interesting to know how well the discriminator actually performs after training.  Coming back to the goal of learning powerful features: The method does not reach state-of-the-art performance on most evaluated tasks (Table 2 and 3) but performs competitive and it would be interesting to see how much this improves if the BiGAN training (and the convolutional architecture used) would be improved.  The paper is very well written and provides most necessary details, although some more details on the training (learning rates, initialization) would be helpful for reproducing the results.  Overall I think the paper provides a very interesting framework for further research, even though the results presented here are not too impressive both with respect to the feature evaluation (and the GAN learning).  Minor: It might be helpful to highlight the best performance numbers in Tables 2 and 3.

SOLUTION:
en