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: In this paper, the authors propose a Bayesian variant of the skipgram model to learn word embeddings. There are two important variant compared to the original model. First, aligned sentences from multiple languages are used to train the model. Therefore, the context words of a given target word can be either from the same sentence, or from an aligned sentence in a different language. This allows to learn multilingual embedding. The second difference is that each word is represented by multiple vectors, one for each of its different senses. A latent variable z models which sense should be used, given the context.  Overall, I believe that the idea of using a probabilistic model to capture polysemy is an interesting idea. The model introduced in this paper is a nice generalization of the skipgram model in that direction. However, I found the paper a bit hard to follow. The formulation might probably be simplified (e.g. why not consider a target word w and a context c, where c is either in the source or target language. Since all factors are independent, this should not change the model much, and would make the presentation easier). The performance of all models reported in Table 2 & 3 seem pretty low.  Overall, I like the main idea of the paper, which is to represent word senses by latent variables in a probabilistic model. I feel that the method could be presented more clearly, which would make the paper much stronger. I also have some concerns regarding the experimental results.  Pros: Interesting extension of skipgram to capture polysemy. Cons: The paper is not clearly written. Results reported in the paper seems pretty low.

Answer: en


Question: I reviewed the manuscript as of December 6th.  The authors perform a systematic investigation of various retraining methods for making a classification network robust to adversarial examples. The authors achieve lower error rates using their RAD and IAEC methods perform better then previously introduced distillation methods for retraining networks to be robust to adversarial examples. This method suggests a promising direction for building a defense for adversarial examples.  Major Comments: I find the paper to not be lacking in exposition and clarity. The paper has a laundry list of related results (page 2) but no clear message. I *think* one larger point is the superior performance of their retraining techniques but it is not clear how well these techniques perform compared to other retraining techniques, nor are the details of the retraining techniques clear. The paper requires more discussion and a clear exposition about the methods the authors introduced (i.e. RAD, IAEC). What follow are some more detailed comments along this theme of improving the exposition and clarity:  - The authors should provide more details about how they constructed the auto-encoder in the IAEC method (diagram?). The same needs to be said for the RAD method. The authors point to a previous workshop submission (

Answer: en


Question: El artículo presenta un estudio de la percepción que tienen los ingenieros de requisitos (noveles y expertos) en relación a la efectividad de las diferentes técnicas de educción  para el descubrimiento de los requisitos en un proyecto de desarrollo de software.  El articulo trata un tema que merece cierta atención ante la gran cantidad de técnicas de educción que existen y la desorientación de los ingenieros de requisitos respecto de cuál utilizar en un determinado contexto.  El estudio realizado considera como noveles Ingenieros que no tienen conocimiento de técnicas de educción y uno de los atributos que debe ser calificado como base del estudio comparativo, es precisamente su experiencia. De hecho como conclusión se expresa que “una más amplia formación y principalmente de tipo práctica, es necesaria para que los ingenieros noveles puedan decidir….”, eso es de Perogrullo. Debería ser considera una gama mayor de atributos del contexto como los sugeridos en [14], que permitan conclusiones más veraces.

Answer:
es