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: I reviewed this paper earlier, when it was an ACL 2016 short paper draft. At that point, it had a flaw in the experiment setup, which is now corrected.  Since back then I suggested I'd be willing to accept the draft for another *ACL event provided that the flaw is corrected, I now see no obstacles in doing so.  Another reviewer did point out that the setup of the paper is somewhat artificial if we focus on real low-resource languages, relating to the costs of *finding* vs. *paying* the annotators. I believe this should be exposed in the writeup not to oversell the method.  There are relevant lines of work in annotation projection for extremely low-resource languages, e.g., Johannsen et al. (2016, ACL) and Agic et al. (2015, ACL). It would be nice to reflect on those in the related work discussion for completeness.  In summary, I think this is a nice contribution, and I vote accept.  It should be indicated whether the data is made available. I evaluate those parts in good faith now, presuming public availability of research.

Answer: en


Question: 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.

Answer: en


Question: El artículo lleva a cabo una comparación de performance para implementaciones basadas en GPU y en CPU para el mismo algoritmo. La programación de GPUs es un tema muy actual en HPC.  Existen problemas en la presentación del artículo y las imágenes no están bien contextualizadas.  Implementaciones y comparaciones de performance entre GPU y CPU son un tema ya abordado en trabajos existentes.  El artículo describe en forma relativamente específica la implementación y sus detalles, en conjunto con los problemas encontrados.  El tema de programación GPU es un tema muy actual en computación de alto rendimiento y su comparación con programación basada en CPU me parece relevante.  Sería recomendable mejorar el orden del artículo, relacionando de mejor manera las imágenes y tablas. Existen faltas de ortografía evidentes dentro del contenido del trabajo.

Answer:
es