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].

Q: Quality, Clarity: There is no consensus on this, with the readers having varying backgrounds, and one reviewer commenting that they found it to be unreadable.     Originality, Significance:   The reviews are mixed on this, with the high score (7) acknowledging a lack of expertise on program induction.  The paper is based on the published TerpreT system, and some think that it marginal and contradictory with respect to the TerpreT paper. In the rebuttal, point (3) from the authors points to the need to better understand gradient-based program search, even if it is not always better. This leaves me torn about a decision on this paper, although currently it does not have strong support from the most knowledgeable reviewers.  That said, due to the originality of this work, the PCs are inclined to invite this work to be presented as a workshop contribution.

A: en
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Q: - 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.

A: en
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Q: El artículo presenta una propuesta de Reparación de Inconsistencias en Data Warehouses usando restricciones que tienen sentido semántico. Se proponen restricciones de prioridad y seguras, además de usar heurísticas previamente definidas en "Repairing dimensions hierarchies under inconsistent reclassification" para la computación de las r-reparaciones.  Los ejemplos que se utilizan para explicar la propuesta son claros. El artículo está bien escrito.

A:
es
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