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: El trabajo presentado a Infonor - Chile, cumple con las expectativas siguientes: 1. Realiza una acertada discusión bibliográfica 2. Incorpora un algoritmo híbrido de búsqueda novedosa 3. Los resultados obtenidos con scheduling con restricciones usando el algoritmo (recursos limitados) muestra resultados alentadores.

A: es
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Q: This paper delves into the mathematical properties of the skip-gram model, explaining the reason for its success on the analogy task and for the general superiority of additive composition models. It also establishes a link between skip-gram and Sufficient Dimensionality Reduction.  I liked the focus of this paper on explaining the properties of skip-gram, and generally found it inspiring to read. I very much appreciate the effort to understand the assumptions of the model, and the way it affects (or is affected by) the composition operations that it is used to perform. In that respect, I think it is a very worthwhile read for the community.  My main criticism is however that the paper is linguistically rather naive. The authors' use of 'compositionality' (as an operation that takes a set of words and returns another with the same meaning) is extremely strange. Two words can of course be composed and produce a vector that is a) far away from both; b) does not correspond to any other concept in the space; c) still has meaning (productivity wouldn't exist otherwise!) Compositionality in linguistic terms simply refers to the process of combining linguistic constituents to produce higher-level constructs. It does not assume any further constraint, apart from some vague (and debatable) notion of semantic transparency. The paper's implication (l254) that composition takes place over sets is also wrong: ordering matters hugely (e.g. 'sugar cane' is not 'cane sugar'). This is a well-known shortcoming of additive composition.   Another important aspect is that there are pragmatic factors that make humans prefer certain phrases to single words in particular contexts (and the opposite), naturally changing the underlying distribution of words in a large corpus. For instance, talking of a 'male royalty' rather than a 'king' or 'prince' usually has implications with regard to the intent of the speaker (here, perhaps highlighting a gender difference). This means that the equation in l258 (or for that matter the KL-divergence modification) does not hold, not because of noise in the data, but because of fundamental linguistic processes. This point may be addressed by the section on SDR, but I am not completely sure (see my comments below).  In a nutshell, I think the way that the authors present composition is flawed, but the paper convinces me that this is indeed what happens in skip-gram, and I think this is an interesting contribution.   The part about Sufficient Dimensionality Reduction seems a little disconnected from the previous argument as it stands. I'm afraid I wasn't able to fully follow the argument, and I would be grateful for some clarification in the authors' response. If I understand it well, the argument is that skip-gram produces a model where a word's neighbours follow some exponential parametrisation of a categorical distribution, but it is unclear whether this actually reflects the distribution of the corpus (as opposed to what happens in, say, a pure count-based model). The fact that skip-gram performs well despite not reflecting the data is that it implements some form of SDR, which does not need to make any assumption about the underlying form of the data. But then, is it fair to say that the resulting representations are optimised for tasks where geometrical regularities are important, regardless of the actual pattern of the data? I.e. there some kind of denoising going on?  Minor comments:  - The abstract is unusually long and could, I think, be shortened.  - para starting l71: I think it would be misconstrued to see circularity here. Firth observed that co-occurrence effects were correlated with similarity judgements, but those judgements are the very cognitive processes that we are trying to model with statistical methods. Co-occurrence effects and vector space word representations are in some sense 'the same thing', modelling an underlying linguistic process we do not have direct observations for. So pair-wise similarity is not there to break any circularity, it is there because it better models the kind of judgements humans known to make.  - l296: I think 'paraphrase' would be a better word than 'synonym' here, given that we are comparing a set of words with a unique lexical item.  - para starting l322: this is interesting, and actually, a lot of the zipfian distribution (the long tail) is fairly uniform.  - l336: it is probably worth pointing out that the analogy relation does not hold so well in practice and requires to 'ignore' the first returned neighbour of the analogy computation (which is usually one of the observed terms).  - para starting l343: I don't find it so intuitive to say that 'man' would be a synonym/paraphrase of anything involving 'woman'. The subtraction involved in the analogy computation is precisely not a straightforward composition operation, as it involves an implicit negation.   - A last, tiny general comment. It is usual to write p(w|c) to mean the probability of a word given a context, but in the paper 'w' is actually the context and 'c' the target word. It makes reading a little bit harder... Perhaps change the notation?  Literature:  The claim that Arora (2016) is the only work to try and understand vector composition is a bit strong. For instance, see the work by Paperno & Baroni on explaining the success of addition as a composition method over PMI-weighted vectors:  D. Paperno and M. Baroni. 2016. When the whole is less than the sum of its parts: How composition affects PMI values in distributional semantic vectors. Computational Linguistics 42(2): 345-350.  *** I thank the authors for their response and hope to see this paper accepted.

A: en
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Q: This paper is a well written paper. This paper can be divided into 2 parts: 1.Adversary training on ImageNet  2.Empirical study of label leak, single/multiple step attack, transferability and importance of model capacity  For part [1], I don’t think training without clean example will not make reasonable ImageNet level model. Ian’s experiment in “Explaining and Harnessing Adversarial Examples” didn't use BatchNorm, which may be important for training large scale model. This part looks like an extension to Ian’s work with Inception-V3 model. I suggest to add an experiment of training without clean samples.  For part [2], The experiments cover most variables in adversary training, yet lack technical depth.  The depth, model capacity experiments can be explained by regularizer effect of adv training;  Label leaking is novel; In transferability experiment with FGSM, if we do careful observe on some special MNIST FGSM example, we can find augmentation effect on numbers, which makes grey part on image to make the number look more like the other numbers. Although this effect is hard to be observed with complex data such as CIFAR-10 or ImageNet, they may be related to the authors' observation "FGSM examples are most transferable".    In this part the authors raise many interesting problems or guess, but lack theoretical explanations.   Overall I think these empirical observations are useful for future work.

A:
en
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