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]: First, I'd like to thank the authors for their answers and clarifications. I find, the presentation of the multi-stage version of the model much clearer now. Pros: + The paper states a sparse coding problem using cosine loss, which allows to solve the problem in a single pass. + The energy-based formulation allows bi-directional coding that incorporates top-down and bottom-up information in the feature extraction process. Cons: + The cost of running the evaluation could be large in the multi-class setting, rendering the approach less attractive and the computational cost comparable to recurrent architectures. + While the model is competitive and improves over the baseline, the paper would be more convincing with other comparisons (see text). The experimental evaluation is limited (a single database and a single baseline) ------ The motivation of the sparse coding scheme is to perform inference in a feed forward manner. This property does not hold in the multi stage setting, thus optimization would be required (as clarified by the authors). Having an efficient way of performing a bi-directional coding scheme is very interesting. As the authors clarified, this could not necessarily be the case, as the model needs to be evaluated many times for performing a classification. Maybe an interesting combination would be to run the model without any class-specific bias, and evaluation only the top K predictions with the energy-based setting. Having said this, it would be good to include a discussion (if not direct comparisons) of the trade-offs of using a model as the one proposed by Cao et al. Eg. computational costs, performance. Using the bidirectional coding only on the top layers seems reasonable: one can get a good low level representation in a class agnostic way. This, however could be studied in more detail, for instance showing empirically the trade offs. If I understand correctly, now only one setting is being reported. Finally, the authors mention that one benefit of using the architecture derived from the proposed coding method is the spherical normalization scheme, which can lead to smoother optimization dynamics. Does the baseline (or model) use batch-normalization? If not, seems relevant to test. Minor comments: I find figure 2 (d) confusing. I would not plot this setting as it does not lead to a function (as the authors state in the text).
[A]: en

[Q]: The paper introduces a simple and effective method for morphological paradigm completion in low-resource settings. The method uses a character-based seq2seq model trained on a mix of examples in two languages: a resource-poor language and a closely-related resource-rich language; each training example is annotated with a paradigm properties and a language ID. Thus, the model enables transfer learning across languages when the two languages share common characters and common paradigms. While the proposed multi-lingual solution is not novel (similar architectures have been explored in syntax, language modeling, and MT), the novelty of this paper is to apply the approach to morphology. Experimental results show substantial improvements over monolingual baselines, and include a very thorough analysis of the impact of language similarities on the quality of results. The paper is interesting, very clearly written, I think it'll be a nice contribution to the conference program. Detailed comments: My main question is why the proposed general multilingual methodology was limited to pairs of languages, rather than to sets of similar languages? For example, all Romance languages could be included in the training to improve Spanish paradigm completion, and all Slavic languages with Cyrillic script could be mixed to improve Ukrainian. It would be interesting to see the extension of the models from bi-lingual to multilingual settings. I think Arabic is not a fair (and fairly meaningless) baseline, given how different is its script and morphology from the target languages. A more interesting baseline would be, e.g., a language with a partially shared alphabet but a different typology. For example, a Slavic language with Latin script could be used as a baseline language for Romance languages. If Arabic is excluded, and if we consider a most distant language in the same the same family as a baseline, experimental results are still strong. A half-page discussion of contribution of Arabic as a regularizer also adds little to the paper; I'd just remove Arabic from all the experiments and would add a regularizer (which, according to footnote 5, works even better than adding Arabic as a transfer language). Related work is missing a line of work on "language-universal" RNN models that use basically the same approach: they learn shared parameters for inputs in multiple languages, and add a language tag to the input to mediate between languages. Related studies include a multilingual parser (Ammar et al., 2016), language models (Tsvetkov et al., 2016), and machine translation (Johnson et al., 2016 ) Minor: I don't think that the claim is correct in line 144 that POS tags are easy to transfer across languages. Transfer of POS annotations is also a challenging task. References: Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah A. Smith. "Many languages, one parser." TACL 2016. Yulia Tsvetkov, Sunayana Sitaram, Manaal Faruqui, Guillaume Lample, Patrick Littell, David Mortensen, Alan W. Black, Lori Levin, and Chris Dyer. "Polyglot neural language models: A case study in cross-lingual phonetic representation learning." NAACL 2016. Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat et al. "Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation." arXiv preprint arXiv:1611.04558 2016. -- Response to author response: Thanks for your response & I'm looking forward to reading the final version!
[A]: en

[Q]: This paper describes a way to speed up convergence through sudden increases of otherwise monotonically decreasing learning rates. Several techniques are presented in a clear way and parameterized method is proposed and evaluated on the CIFAR task. The concept is easy to understand and the authors chose state-of-the-art models to show the performance of their algorithm. The relevance of these results goes beyond image classification. Pros: - Simple and effective method to improve convergence - Good evaluation on well known database Cons: - Connection of introduction and topic of the paper is a bit unclear - Fig 2, 4 and 5 are hard to read. Lines are out of bounds and maybe only the best setting for T_0 and T_mult would be clearer. The baseline also doesn't seem to converge Remarks: An loss surface for T_0 against T_mult would be very helpful. Also understanding the relationship of network depth and the performance of this method would add value to this analysis.
[A]:
en