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].
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Question: - 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.

Answer: en


Question: RESUMEN:  Este trabajo presenta/ofrece una aplicación Web móvil que permite, a través de una Geo-Localización del dispositivo, crear un mapa del crimen en la ciudad de Lima, Perú.   EVALUACIÓN GENERAL:  El documento no sigue la estructura de un artículo científico y parece que tampoco sigue el formato de INFONOR. Además, el trabajo presentado parece más bien un trabajo de ingeniería que científico. Aunque esta aplicación Web móvil podría ser interesante,  se debe mencionar no ofrece ninguna dirección URL donde (com)probar la aplicación presentada.  La Sección 2 debería discutir el trabajo relacionado y algunos conceptos, pero en el medio de esta sección, se describe parte de la propuesta. Además, el trabajo define en casi una columna qué es la delincuencia usando extensas citas (“ … ”).  La sección discusión de experimentos y resultados no se describe experimentos ni resultados (estos últimos son descritos pero no discutidos), solo se muestra algunas facetas/pantallas de la aplicación. Además, justifica el uso de Android a través de una cita muy larga (“ … “).   DETALLES MENORES:  - El abstract en Ingles puede ser mejorable, comenzando por eliminar la primera palabra del paper. Se pudo escribir en español el abstract. - El formato del documento no sigue el estándar de INFONOR, especialmente con los márgenes, - En algunas partes del documento, se puede observar la palabra “tesis” y no artículo. En otras partes del texto, se puede observar la palabra “ponencia”. - La Figura 1 no aporta en casi nada al documento. - Después de cada título, se puede observar “:” que no es necesario. - La sección 2 usa de manera excesiva citas “…” - La sección 2.2 habla sobre tipos de delincuencia, pero en los sub-títulos de esta sección dice “Análisis” - El título de la sección 2.3 choca con el texto de la columna de la izquierda - La tabla 2, la cual menciona a las aplicaciones, no es evaluadas.

Answer: es


Question: Two things I really liked about this paper: 1. The whole idea of having a data-dependent proposal distribution for MCMC. I wasn't familiar with this, although it apparently was previously published. I went back: the (Zhu, 2000) paper was unreadable. The (Jampani, 2014) paper on informed sampling was good. So, perhaps this isn't a good reason for accepting to ICLR.  2. The results are quite impressive. The rough rule-of-thumb is that optimization can help you speed up code by 10%. The standard MCMC results presented on the paper on randomly-generated programs roughly matches this (15%). The fact that the proposed algorithm get ~33% speedup is quite surprising, and worth publishing.  The argument against accepting this paper is that it doesn't match the goals of ICLR. I don't go to ICLR to hear about generic machine learning papers (we have NIPS and ICML for that). Instead, I go to learn about how to automatically represent data and models. Now, maybe this paper talks about how to represent (generated) programs, so it tangentially lives under the umbrella of ICLR. But it will compete against more relevant papers in the conference -- it may just be a poster. Sending this to a programming language conference may have more eventual impact.  Nonetheless, I give this paper an "accept", because I learned something valuable and the results are very good.

Answer:
en