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].

Example Input: This paper gives a theoretical motivation for tieing the word embedding and output projection matrices in RNN LMs. The argument uses an augmented loss function which spreads the output probability mass among words with close word-embedding.   I see two main drawbacks from this framework: The augmented loss function has no trainable parameters and is used for only for regularization. This is not expected to give gains with large enough datasets.  The augmented loss is heavily “engineered” to produce the desired result of parameter tying. It’s not clear what happens if you try to relax it a bit, by adding parameters, or estimating y~ in a different way.   Nevertheless the argument is very interesting, and clearly written. The simulated results indeed validate the argument, and the PTB results seem promising.  Minor comments: Section 3: Can you clarify if y~ is conditioned on the t example or on the entire history. Eq. 3.5: i is enumerated over V (not |V|)
Example Output: en

Example Input: *** 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
Example Output: en

Example Input: En este trabajo se realiza una propuesta exploratoria sobre un perfil de adecuación de técnicas de educción de requisitos de software. El trabajo es una extensión de un trabajo previo.  Se recomienda mejorar algunos aspectos de este manuscrito como son: -Describir a más detalle el trabajo previo en el que se basa este escrito y mencionar las diferencias y contenido nuevo. -Consistencia entre los términos requisitos y requerimientos, en la pág. 3 se emplea el término requerimientos. -En la medida de lo posible mantener términos en castellano. -Añadir acento en "solo" siempre y cuando se refiera a "únicamente". -Corregir "Lo" por "Los" final de pág. 4, después de ref. 15., añadir acento a "comparara", en misma pág., corregir "cuatros" final pág. 6. -Corregir alineación de texto en primeros párrafos de ambas columnas en pág. 10, el primer párrafo de la segunda columna debería estar en la primera columna.
Example Output:
es