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].

Es un trabajo difícil de catalogar, presenta un tema usualmente asociado a bases de datos deductivas (reparación), donde deben existir reglas o restricciones de integridad que han sido violadas, adaptado al tema de inconsistencia producida por nuevos hechos en un DW que no se ajustan correctamente a sus dimensiones. Esto, normalmente es tratado en este ámbito mediante el uso de Slowly Changing Dimensions (SCD), algo que extrañamente no es nombrado en el artículo.  Aun así, y asumiendo que el problema realmente existe y tiene aplicabilidad, es presentado muy superficialmente, y termina finalmente en una propuesta informal que no es descrita rigurosamente como un algoritmo en sí. Me parece que es una propuesta que aún se encuentra en un estado muy inicial y cuya problemática no logra convencerme como tal.
es

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.
en

This paper proposes a character-aware attention residual network for sentence embedding. Several text classification tasks are used to evaluate the effectiveness of the proposed model. On two of the three tasks, the residual network outforms a few baselines, but couldn't beat the simple TFIDF-SVM on the last one.  This work is not novel enough. Character information has been applied in many previously published work, as cited by the authors. Residual network is also not new.  Why not testing the model on a few more widely used datasets for short text classification, such as TREC? More competitive baselines can be compared to. Also, it's not clear how the "Question" dataset was created and which domain it is.  Last, it is surprising that the format of citations throughout the paper is all wrong.   For example: like Word2Vec Mikolov et al. (2013) -> like Word2Vec (Mikolov et al., 2013)  The citations can't just mix with the normal text. Please refer to other published papers.

en