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].
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Question: This paper introduces UCCA as a target representation for semantic parsing and also describes a quite successful transition-based parser for inference into that representation. I liked this paper a lot. I believe there is a lot of value simply in the introduction of UCCA (not new, but I believe relatively new to this community), which has the potential to spark new thinking about semantic representations of text. I also think the model was well thought out. While the model itself was fairly derivative of existing transition-based schemes, the extensions the authors introduced to make the model applicable in this domain were reasonable and well-explained, at what I believe to be an appropriate level of detail.  The empirical evaluation was pretty convincing -- the results were good, as compared to several credible baselines, and the authors demonstrated this performance in multiple domains. My biggest complaint about this paper is the lack of multilingual evaluation, especially given that the formalism being experimented with is exactly one that is supposed to be fairly universal. I'm reasonably sure multilingual UCCA corpora exist (in fact, I think the "20k leagues" corpus used in this paper is one such), so it would be good to see results in a language other than English.  One minor point: in section 6, the authors refer to their model as "grammarless", which strikes me as not quite correct. It's true that the UCCA representation isn't derived from linguistic notions of syntax, but it still defines a way to construct a compositional abstract symbolic representation of text, which to me, is precisely a grammar. (This is clearly a quibble, and I don't know why it irked me enough that I feel compelled to address it, but it did.)  Edited to add: Thanks to the authors for their response.

Answer: en


Question: This paper proposes a neural architecture for answering non-factoid questions. The author's model improves over previous neural models for answer sentence selection. Experiments are conducted on a Japanese love advice corpus; the coolest part of the paper for me was that the model was actually rolled out to the public and its answers were rated twice as good as actual human contributors!   It was hard for me to determine the novelty of the contribution. The authors mention that their model "fills the gap between answer selection and generation"; however, no generation is actually performed by the model! Instead, the model appears to be very similar to the QA-LSTM of Tan et al., 2015 except that there are additional terms in the objective to handle conclusion and supplementary sentences. The structure of the answer is fixed to a predefined template (e.g., conclusion --> supplementary), so the model is not really learning how to order the sentences. The other contribution is the "word embedding with semantics" portion described in sec 4.1, which is essentially just the paragraph vector model except with "titles" and "categories" instead of paragraphs.   While the result of the paper is a model that has actually demonstrated real-life usefulness, the technical contributions do not strike me as novel enough for publication at ICLR.  Other comments: - One major issue with the reliance of the model on the template is that you can't evaluate on commonly-used non-factoid QA datasets such as InsuranceQA. If the template were not fixed beforehand (but possibly learned by the model), you could conceivably evaluate on different datasets.  - The examples in Table 4 don't show a clear edge in answer quality to your model; QA-LSTM seems to choose good answers as well. - Doesn't the construction model have an advantage over the vanilla QA-LSTM in that it knows which sentences are conclusions and which are supplementary? Or does QA-LSTM also get this distinction?

Answer: en


Question: *** Paper Summary ***  The paper proposes to a new neural network architecture. The layer weights of a classical network are computed as a function of a latent representation associated with the layer. Two instances are presented (i) a CNN where each layer weight is computed from a lower dimensional layer embedding vector; (ii) an RNN where each layer weight is computed from a secondary RNN state.  *** Review Summary ***  Pros:  - I like the idea of bringing multiplicative RNNs and their predecessors back into the spotlight.  - LM and MT results are excellent.  Cons:   - The paper could be better written. It is too long for the conference format and need refocussing.  - On related work, the relation with multiplicative RNN and their generic tensor product predecessor (Order 2 networks, wrt C. Lee Giles definition) should be mentioned in the related work section and the differences with earlier research need to be explained and motivated (by the way it is better to say that something is revisiting an old idea or training it at modern scale/on modern tasks than ommitting it). - on focus, it is not clear if your goal is to achieve better performance or more compact networks. In the RNN section you lean toward the former, in the CNN section you seem to lean toward the latter.  I would suggest to make the paper shorter and clearer possibly leaving the CNN results for latter publication. The relation with multiplicative/order 2 networks and eventual differences need to be explained.  *** Detailed Review ***  Multiplicative networks are an extremely powerfull architecture and bringing them back into the spotlight is excellent. This paper has excellent results but suffer poor presentation, lack of a clear focus. It spends time on details and ommit important points. In its current form, it is much too long to long and his not self contained without the appendices.  Spending more time on multiplicative RNNs, order 2 networks at the begining of the paper would be excellent. This will let you highlight the difference between this paper and earlier work. It would also be necessary to spend a little time on why multiplicative RNN were less used than gated RNN: it seems that the optimization problem their training involve is tricker and it would be helpful to explain whether you had a harder time tweaking optimization parameters or whether you needed longer training sessions compared to LSTMs, regular CNN. On name, I am not sure that "hypernetwork" help the reader understand better what the proposed architecture compared to multiplicative interactions.  In section 3.2, you seem to imply that there are different settings of hypernetworks that allow to vary from an RNN to a CNN, this is not clear to me, maybe you could show how this would work on a simple temporal problem with equations.   The work on CNN and RNN are rather disconnected to me: for CNN, you seem to be interested in a low rank structure of the weights, showing that similar performance can be achieved with less weights. It is not clear to me why to pursue that goal. Do you expect speedups? less memory for embedded applications? In that case you should compare with alternative strategies, e.g. model compression (Caruana et al 2006, aka Dark Knowledge, Hinton et al 2014) or hashed networks (Chen et al 2015).   For RNN, you seem to target better perplexity/BLEU and model compactness is not a priority. Instead of making the weights have a simpler structure, you make them richer, i.e. dependent over time. It seems in that case models might be bigger and take longer to train. You might want to comment on training time, inference time, memory requirement in that case, as you highlight it might be an important goal in the CNN section. Overall, I am not sure it helps to have this mixed message. I would rather see the paper fit in the conference format with the RNN results alone and a clearer explanation and defers the publications of the CNN results when a proper comparison with memory concerned methods is performed.  Some of the discussions are not clear to me, I am not sure what message the reader should get from Figure 2 or from the discussion on saturation statistics (p10, Figure 5). Similarly, I am not sure if Figure 4 is showing anything: everything should change more drastically at word boundaries even in a regular LSTM (states, gates units should look very different before/after a space); without such a comparison it is hard to see if this is unique to your network.  The results on handwriting generation are harder to compare for me. Log-loss are hard to understand, I have no sense whether the difference between models is significant (what would be the variance in this metric under boostrap sampling of the training set?). I am not sold either on qualitative metric were human can assess quality but human cannot evaluate if the network is repeating the training set. Did you thing at precision/recall metric for ink, possibly with some spatial tolerance ? (e.g. evaluation of segmentation tasks in vision).  The MT experiments are insufficiently discussed in the main text.  Overall, I would suggest to make the paper shorter and clearer possibly leaving the CNN results for latter publication. You need to properly discuss the relation to multiplicative/order 2 networks and highlight the differences. Unclear discussion can be eliminated to make the experimental setup and the results presentation clearer in the main text.  *** References ***  M.W. Goudreau, C.L. Giles, S.T. Chakradhar, D. Chen, "First-Order Vs. Second-Order Single Layer Recurrent Neural Networks,"IEEE Trans. on Neural Networks, 5 (3), p. 511, 1994.  Cristian Bucila, Rich Caruana, and Alexandru Niculescu-Mizil, "Model Compression," The Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006), August 2006, pp. 535-541.  Dark knowledge, G Hinton, O Vinyals, J Dean 2014  W. Chen, J. Wilson, S. Tyree, K. Weinberger and Y. Chen, Compressing Neural Networks with the Hashing Trick, Proc. International Conference on Machine Learning (ICML-15)

Answer:
en