TASK DEFINITION: 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].
PROBLEM: 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.

SOLUTION: en

PROBLEM: In this paper, the authors propose a new method to learn hierarchical representations of sentences, based on reinforcement learning. They propose to learn a neural shift-reduce parser, such that the induced tree structures lead to good performance on a downstream task. They use reinforcement learning (more specifically, the policy gradient method REINFORCE) to learn their model. The reward of the algorithm is the evaluation metric of the downstream task. The authors compare two settings, (1) no structure information is given (hence, the only supervision comes from the downstream task) and (2) actions from an external parser is used as supervision to train the policy network, in addition to the supervision from the downstream task. The proposed approach is evaluated on four tasks: sentiment analysis, semantic relatedness, textual entailment and sentence generation.  I like the idea of learning tree representations of text which are useful for a downstream task. The paper is clear and well written. However, I am not convinced by the experimental results presented in the paper. Indeed, on most tasks, the proposed model is far from state-of-the-art models:  - sentiment analysis, 86.5 v.s. 89.7 (accuracy);  - semantic relatedness, 0.32 v.s. 0.25 (MSE);  - textual entailment, 80.5 v.s. 84.6 (accuracy). From the results presented in the paper, it is hard to know if these results are due to the model, or because of the reinforcement learning algorithm.  PROS:  - interesting idea: learning structures of sentences adapted for a downstream task.  - well written paper. CONS:  - weak experimental results (do not really support the claim of the authors).  Minor comments: In the second paragraph of the introduction, one might argue that bag-of-words is also a predominant approach to represent sentences. Paragraph titles (e.g. in section 3.2) should have a period at the end.  ---------------------------------------------------------------------------------------------------------------------- UPDATE  I am still not convinced by the results presented in the paper, and in particular by the fact that one must combine the words in a different way than left-to-right to obtain state of the art results. However, I do agree that this is an interesting research direction, and that the results presented in the paper are promising. I am thus updating my score from 5 to 6.

SOLUTION: en

PROBLEM: El articulo muestra los resultados empíricos de la integración (acoplamiento) del algoritmo apriori en un SGBD.  Indican que el trabajo expuesto se basa en el trabajo de Agrawal & Shim, este trabajo es del año 1996, al respecto ¿no hay trabajos más recientes? , ¿Se puede escoger otro trabajo más reciente? , ¿Por qué escoger este trabajo? ¿Por qué este enfoque?  En la introducción faltan referencias que permitan fundamentar el trabajo como un aporte un poco más cercano a algo científico. Por ejemplo " Existen algunas investigaciones focalizadas"....¿ cuáles ?  El trabajo esta escribo más bien como un experimento técnico, que como un artículo de congreso.  Se valora el tiempo y esfuerzo en realizar las pruebas.

SOLUTION:
es