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]: El artículo presentado, describe someramente los estudios realizados y decisiones adoptadas, que permiten dotar de una infraestructura de alta capacidad a los observatorios ESO Paranal y Cerro Armazones, ubicados en el desierto de atacama.  Se presentan las diversas etapas involucradas en el desafío por dotar de un gran ancho de banda a los mencionados observatorios ubicados en aéreas no pobladas (pleno desierto) para su conexión expedita con diversas redes académicas y la comunidad astronómica europea lo cual constituye un buen desafío desde el punto de vista tecnológico.  Sería importante destacar porqué de la necesidad de contar con grandes anchos de banda, es decir describir en mayor detalle el tipo de tráfico que lo justifica, por ejemplo monitoreo, control y operación remota, etc.  Aún no está en operación el enlace
[EX A]: es

[EX Q]: *** Paper Summary ***  This paper applies adversarial and virtual adversarial training to LSTM for text classification. Since text inputs are discrete adversarial perturbation are applied to the (normalized) word embeddings. Extensive experiments are reported and demonstrate the advantage of these methods.  *** Review Summary ***  The paper reads well and has sufficent references. The application of adversarial training to text data is a simple but not trivial extension. The experimental section presents extensive experiments with comparison to alternative strategies. The proposed method is simple and effective and can be easily be applied after reading the paper.  *** Detailed Review ***  The paper reads well. I have only a few comments regarding experiments and link to prior resarch:  Experiments:  - In Table 2 (and for other datasets as well), could you include an SVM baseline? e.g. S Wang and C Manning 2012? - As another baseline, did you consider dropping words, i.e. masking noise? It is generally better than dropout/gaussian noise for text application (e.g. denoising autoencoders)? - I am not sure I understand why virtual adversarial is worse than the baseline in Table 5. If you tune epsilon, in the worse case you would get the same performance as the baseline? Was it that validation was unreliable?  Related Work:  I think it would be interesting to point at SVM, transductive SVM who achieve something similar to adversarial training. When maximizing the margin in a (transductive) SVM, it is equivalent to move the example toward the decision boundary, i.e. moving them in the direction of increase of the loss gradient.  Also it would be interesting to draw a parallel between adversarial training and contrastive divergence. The adversarial samples are very close in nature to the one step Markov Chain samples from CD. See Bengio 2009. Related to this technique are also approaches that try to explicitely cancel the Jacobian at data points, e.g. Rifai et al 2011.  *** References ***  Marginalized Denoising Autoencoders for Domain Adaptation. Minmin Chen, K Weinberger. Stacked Denoising Autoencoders. Pascal Vincent. JMLR 2011. Learning invariant features through local space contraction, Salah Rifai, Xavier Muller, Xavier Glorot, Gregoire Mesnil, Yoshua Bengio and Pascal Vincent, 2011. Learning Deep Architectures for AI, Yoshua Bengio 2009 Large Scale Transductive SVMs. Ronan Collobert et al 2006 Optimization for Transductive SVM.  O Chapelle, V Sindhwani, SS Keerthi JMLR 2008
[EX A]: en

[EX Q]: The paper proposed to analyze several recently developed machine readers and found that some machine readers could potentially take advantages of the entity marker (given that the same marker points out to the same entity). I usually like analysis papers, but I found the argument proposed in this paper not very clear.  I like the experiments on the Stanford reader, which shows that the entity marker in fact helps the Stanford reader on WDW. I found that results rather interesting.  However, I found the organization and the overall message of this paper quite confusing. First of all, it feels that the authors want to explain the above behavior with some definition of the “structures”. However, I am not sure that how successful the attempt is. For me, it is still not clear what the structures are. This makes reading section 4 a bit frustrating.   I am also not sure what is the take home message of this paper. Does it mean that the entity marking should be used in the MR models? Should we design models that can also model the entity reference at the same time? What are the roles of the linguistic features here? Should we use linguistic structure to overcome the reference issue?  Overall, I feel that the analysis is interesting, but I feel that the paper can benefit from having a more focused argument.
[EX A]:
en