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].

[EX Q]: This paper gives a theoretical motivation for tieing the word embedding and output projection matrices in RNN LMs. The argument uses an augmented loss function which spreads the output probability mass among words with close word-embedding.   I see two main drawbacks from this framework: The augmented loss function has no trainable parameters and is used for only for regularization. This is not expected to give gains with large enough datasets.  The augmented loss is heavily “engineered” to produce the desired result of parameter tying. It’s not clear what happens if you try to relax it a bit, by adding parameters, or estimating y~ in a different way.   Nevertheless the argument is very interesting, and clearly written. The simulated results indeed validate the argument, and the PTB results seem promising.  Minor comments: Section 3: Can you clarify if y~ is conditioned on the t example or on the entire history. Eq. 3.5: i is enumerated over V (not |V|)
[EX A]: en

[EX Q]: This paper describes how to approximate the FastText approach such that its memory footprint is reduced by several orders of magnitude, while preserving its classification accuracy. The original FastText approach was based on a linear classifier on top of bag-of-words embeddings. This type of method is extremely fast to train and test, but the model size can be quite large.  This paper focuses on approximating the original approach with lossy compression techniques. Namely, the embeddings and classifier matrices A and B are compressed with Product Quantization, and an aggressive dictionary pruning is carried out. Experiments on various datasets (either with small or large number of classes) are conducted to tune the parameters and demonstrate the effectiveness of the approach. With a negligible loss in classification accuracy, an important reduction in term of model size (memory footprint) can be achieved, in the order of 100~1000 folds compared to the original size.  The paper is well written overall. The goal is clearly defined and well carried out, as well as the experiments. Different options for compressing the model data are evaluated and compared (e.g. PQ vs LSH), which is also interesting. Nevertheless the paper does not propose by itself any novel idea for text classification. It just focuses on adapting existing lossy compression techniques, which is not necessarily a problem. Specifically, it introduces:   - a straightforward variant of PQ for unnormalized vectors,   - dictionary pruning is cast as a set covering problem (which is NP-hard), but a greedy approach is shown to yield excellent results nonetheless,   - hashing tricks and bloom filter are simply borrowed from previous papers.  These techniques are quite generic and could as well be used in other works.    Here are some minor problems with the paper:    - it is not made clear how the full model size is computed. What is exactly in the model? Which proportion of the full size do the A and B matrices, the dictionary, and the rest, account for? It is hard to follow where is the size bottleneck, which also seems to depend on the target application (i.e. small or large number of test classes). It would have been nice to provide a formula to calculate the total model size as a function of all parameters (k,b for PQ and K for dictionary, number of classes).      - some parts lack clarity. For instance, the greedy approach to prune the dictionary is exposed in less than 4 lines (top of page 5), though it is far from being straightforward. Likewise, it is not clear why the binary search used for the hashing trick would introduce an overhead of a few hundreds of KB.     Overall this looks like a solid work, but with potentially limited impact research-wise.
[EX A]: en

[EX Q]: This paper is an extension of Lenc&Vedaldi15 paper, showing CNN representations at FC7 layer are to certain extent equivariant to various classes of transformations and that training with a certain group of transformation makes the representations more equivalent.  Authors performed a large amount of experiments, training over 30 networks with different forms of jitter, which is quite impressive. However it is rather difficult to find a main message of this work. Yes, authors measured the properties on a different layer than the Lenc&Vedaldi15, however it is hard to find some novel insights other than the known fact that jitter helps to achieve invariance. The evaluation seems to be mostly correct, however the paper does not seem to be solving the task advertised in its title really well.  Major issues are in the experiments with the representation distances: * The selection of only FC7 is a bit controversial - it is followed only by a single classification layer (FC8) to the common output - class likelyhoods. Because the FC8 is just a linear projections, what the equivalence map does is just to re-project the FC8 weights of the attached network to the weights of the original network. Probably performing similar experiments but on more layers may be more useful (as the networks are already trained). * The experiment with representation distance is missing what is the classification error on the testing dataset. This would answer whether the representations are actually compatible up to linear transformation at all... * It is not clear for the experiment with K-NN whether this is measured per each test set example? After training the equivalence map? More clear would be to show that networks trained on similar group of jitter transformations are more compatible on the target task. * The proposed method does not seem to improve equivariance consistently on all tasks. Especially with \lambda_1 and \lambda_2 having such small values, the loss is basically equal to simple data jitter as it just adds up the loss of the original and transformed image. Maybe the issue is in the selection of the FC7 layer?  In general, this paper shows some interesting results on the FC7 equivariance, but it does not seem to be drawing many interesting new observations out of these experiments. Due to some issues with the equivalence experiments and the finetuning of equivariance, I would not recommend acceptance of this manuscript. However, refining the experiments on already trained networks and restructuring this manuscript into more investigative work may lead to interesting contribution to the field.  There are also few minor issues: * It is not experimentally verified that the new criterion for equivariance mapping helps to gain better results. * The angles on page 1 and 5 are missing units (degrees?). * On page three, "In practice, it is difficult... ", it is not M_g which is maximised/minimised, but the loss over the M_g * Page 4, footnote 2 - if you are just halving the activations, it is hard to call it a dropout as this constant factor can be passed to the following/preceding weights * Is the network for RVL-CDIP the same architecture as Alexnet? * On page 7, Figure 3a+3b - in my opinion, turning the diagonal elements to white is really misleading, and probably even incorrect, as the distance between the same representations should be zero (which is also a way how to verify that the experiments are performed correctly).
[EX A]:
en