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].

- Strengths:  This is the first neural network-based approach to argumentation mining. The proposed method used a Pointer Network (PN) model with multi-task learning and outperformed previous methods in the experiments on two datasets.  - Weaknesses:  This is basically an application of PN to argumentation mining. Although the combination of PN and multi-task learning for this task is novel, its novelty is not enough for ACL long publication. The lack of qualitative analysis and error analysis is also a major concern.  - General Discussion:  Besides the weaknesses mentioned above, the use of PN is not well-motivated. Although three characteristics of PN were described in l.138-143, these are not a strong motivation against the use of bi-directional LSTMs and the attention mechanism. The authors should describe what problems are solved by PN and discuss in the experiments how much these problems are solved.  Figures 2 and 3 are difficult to understand. What are the self link to D1 and the links from D2 to E1 and D3/D4 to E2? These are just the outputs from the decoder and not links. The decoder LSTM does not have an input from e_j in these figures, but it does in Equation (3). Also, in Figure 3, the abbreviation "FC" is not defined.  Equation (8) is strange. To calculate the probability of each component type, the probability of E_i is calculated.  In the experiments, I did not understand why only "PN", which is not a joint model, was performed for the microtext corpus.  It is not clear whether the BLSTM model is trained with the joint-task objective.  There are some studies on discourse parsing using the attention mechanism. The authors should describe the differences from these studies.  Minor issues:  l.128: should related -> should be related  l.215: (2015) is floating  l.706: it able -> it is able  I raised my recommendation score after reading the convincing author responses. I strongly recommend that the authors should discuss improved examples by PN as well as the details of feature ablation.
en

En  Cap III no veo diferencias o aportes  sustantivos a lo ya manifestado en documento “la necesidad en Chile de una infraestructura tecnológica colaborativa de apoyo a la investigación astronómica” Red Universitaria Nacional, Noviembre 2008. De manera que corresponde citarlo como referencia o sino hacer los aportes o diferencias sustantivas, al igual que [Link]  Finalmente, el artículo carece de relevancia, ya que los aportes no se diferencias de lo que presenta.   Más aún, las figuras no se explican, como tampoco se cita su fuente que están dadas en [Link]   No cabe duda que lo relevante serán los resultados que se obtengan del  proyecto (FONDEF D11I1060, adjudicado el año 2011), en el sentido del diseño e implementación de un observatorio virtual, el cual deberá cumplir con los estándares de IVOA, sin embargo, por ahora, no veo aportes sustantivos.  Además, no se mencionan las "instancias donde presentarán problemáticas", tampoco la o las "problemáticas que enfrentan" como comunidad ante el procesamiento de datos que serán resueltos mediante "técnicas computacionales", las que hasta ahora son desconocidas.  Por otro lado la arquitectura que se muestra y me parece natural es la de IVOA, sin embargo, no se muestran, al menos por ahora, los aportes que se han realizado en este sentido.
es

This paper proposed an iterative query updating mechanism for cloze-style QA. The approach is novel and interesting and while it is only verified in the paper for two Cloze-style tasks (CBT and WDW), the concept of read/compose/write operations seem to be more general and can be potentially applied to other reasoning tasks beyond Cloze-style QA. Another advantage of the proposed model is to learn when to terminate the iteration by the so-called adaptive computation model, such that it avoids the issue of treating the number of iterations as another hyper-parameter, which is a common practice of iterative models/multi-hop reasoning in previous papers.  There are a couple places that this paper can improve. First, I would like to see the results from CNN/Daily Mail as well to have a more comprehensive comparison. Secondly, it will be useful to visualize the entire M^q sequence over time t (not just z or the query gating) to help understand better the query regression and if it is human interpretable.

en