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]: SUMMARY.  The paper proposes a machine reading approach for cloze-style question answering. The proposed system first encodes the query and the document using a bidirectional gru. These two representations are combined together using a Gated Attention (GA). GA calculates the compatibility of each word in the document and the query as a probability distribution. For each word in the document a gate is calculated weighting the query representation according to the word compatibility. Ultimately, the gate is applied to the gru-encoded document word. The resulting word vectors are re-encoded with a bidirectional GRU. This process is performed for multiple hops. After k hops, the probability of a word to be part of the answer is calculated by a log-linear model that take as input the last word representations, and the concatenation of the last query representation before and after the cloze token. The probability of a candidate being the answer to the question is given by a linear combination of the single word probabilities.  The proposed model is tested on 4 different dataset.  The authors shown that the proposed model works well (state-of-the-art performance) for 3 out of 4 benchmarks.   ----------  OVERALL JUDGMENT The main contribution of the paper is the gated attention mechanism, that in my opinion, is a simple and interesting idea. The paper is well thought, and the ablation study on the benefits given by the gated attention are convincing. The GA reader as whole model outperforms previous state-of-the-art models on 3 benchmarks and seems very promising also on the CBT dataset. I would have liked to see some discussion on why the model works less well on the CBT dataset, though.   ----------  DETAILED COMMENTS  minor. In the introduction, Weston et al., 2014 do not use any attention mechanism.
[EX A]: en

[EX Q]: This paper takes a model based on that of Graves and retrofits it with a representation derived from the work of Plamondon.  part of the goal of deep learning has been to avoid the use of hand-crafted features and have the network learn from raw feature representations, so this paper is somewhat against the grain.   The paper relies on some qualitative examples as demonstration of the system, and doesn't seem to provide a strong motivation for there being any progress here.  The paper does not provide true text-conditional handwriting synthesis as shown in Graves' original work.   Be more consistent about your bibliography (e.g. variants of Plamondon's own name, use of "et al." in the bibliography etc.)
[EX A]: en

[EX Q]: Este trabajo propone un nuevo enfoque para el apoyo de la priorización de requisitos a través de un proceso de transformación de los Casos de Uso en un grafo de dependencia a través de la aplicación de técnicas de IR, redes bayesianas y evaluación del experto en el dominio. El enfoque se presenta como un interesante aporte para la priorización de requisitos y muestra su aplicación a un caso real. El documento está bien escrito y muy bien soportado por referencias. Los resultados son robustos según el enfoque utilizado y se presentan trabajos futuros bien direccionados.  Algunos errores tipográficos o de redacción pueden ser encontrados en la página 5 (párrafo 1 y 2). Confusión por el uso de k y K en la página 5 (o error tipográfico). Puede que no quede claro la utilización del conocimiento de la priorización realizada en el caso base de prueba con respecto a la realizada semi-automáticamente por su enfoque, salvo que se utiliza el conocimiento del experto. Quizás una comparación objetiva entre las dos priorizaciones sería adecuado.
[EX A]:
es

