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].

*** 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
en

El artículo presenta el proceso de construcción de un almacén de datos para implementar OLAP sobre datos de explotación de recursos marinos en las costas de Chile.  El problema está claramente delimitado y tiene aplicación práctica relevante para el país.  El trabajo presentado corresponde a un caso de uso muy completo y detalladamente documentado de técnicas bien conocidas de minería de datos.  El ABSTRACT debe ser editado para que represente una traducción apropiada y legible del RESUMEN.  No es evidente el aporte original del trabajo.  Las conclusiones originales del artículo dicen relación con el dominio de la aplicación (estadísticas de explotación de recursos marinos) y no con temas relevantes a la conferencia.  Debería completarse la información faltante en la bibliografía.
es

- Strengths: A nice, solid piece of work that builds on previous studies in a productive way. Well-written and clear.   - Weaknesses:   Very few--possibly avoid some relatively "empty" statements:  191 : For example, if our task is to identify words used similarly across contexts, our scoring function can be specified to give high scores to terms whose usage is similar across the contexts.  537 : It is educational to study how annotations drawn from the same data are similar or different.  - General Discussion: In the first sections I was not sure that much was being done that was new or interesting, as the methods seemed very reminiscent of previous methods used over the past 25 years to measure similarity, albeit with a few new statistical twists, but conceptually in the same vein. Section 5, however, describes an interesting and valuable piece of work that will be useful for future studies on the topic. In retrospect, the background provided in sections 2-4 is useful, if not necessary, to support the experiments in section 5.   In short, the work and results described will be useful to others working in this area, and the paper is worthy of presentation at ACL.  Minor comments:  Word, punctuation missing? 264 : For word annotations, we used PPMI, SVD, and SGNS (skipgram with negative sampling from Mikolov et al. (2013b)) word vectors released by Hamilton et al. (2016).  Unclear what "multiple methods" refers to : 278 : some words were detected by multiple methods with CCLA

en