instruction:
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].
question:
- Strengths: This paper introduced a novel method to improve zero pronoun resolution performance.. The main contributions of this papers are: 1) proposed a simple method to automatically generate a large training set for zero pronoun resolution task; 2) adapted a two step learning process to transfer knowledge from large data set to the specific domain data; 3) differentiate unknown words using different tags. In general, the paper is well written. Experiments are thoroughly designed.   - Weaknesses:  But I have a few questions regarding finding the antecedent of a zero pronoun: 1. How will an antecedent be identified, when the prediction is a pronoun? The authors proposed a method by matching the head of noun phrases. It’s not clear how to handle the situation when the head word is not a pronoun. 2. What if the prediction is a noun that could not be found in the previous contents? 3. The system achieves great results on standard data set. I’m curious is it possible to evaluate the system in two steps? The first step is to evaluate the performance of the model prediction, i.e. to recover the dropped zero pronoun into a word; the second step is to evaluate how well the systems works on finding an antecedent.  I’m also curious why the authors decided to use attention-based neural network. A few sentences to provide the reasons would be helpful for other researchers.  A minor comment: In figure 2, should it be s1, s2 … instead of d1, d2 ….?   - General Discussion: Overall it is a great paper with innovative ideas and solid experiment setup.
answer:
en


question:
El artículo presenta un estudio sobre las tecnologías que pueden influenciar o afectar la seguridad del Software, abarcando los tópicos generalmente tratados en literatura asociada a seguridad informática. El enfoque que se presenta en el artículo, en lo que respecta a seguridad del software es correcto.  El artículo comienza con referencias a varios tópicos y aspectos primordiales de seguridad, sin embargo no se logra asociar claramente la relación entre los conceptos que fundamentan el estudio con el análisis posteriormente realizado referente a la influencia que tiene la tecnología en la Seguridad.  Adicionalmente, hay aspectos que debieran profundizarse (e.g., en la Figura 4 se muestra una distribución a nivel de uso de sistemas operativos, sin embargo, no se dan mayores detalles o implicaciones que podría tener dicha distribución en lo que respecta a Seguridad).  Las conclusiones no dan un real aporte de parte del autor referente al análisis que se pueda concluir después del estudio realizado.  Las referencias bibliográficas en su mayoría corresponden a textos de seguridad informática, pero se recomienda respaldar la revisión bibliográfica con mayor cantidad artículos científicos actuales.
answer:
es


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