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: El artículo presenta el desarrollo de una aplicación móvil, no incluye ningún elemento de investigación asociada a dicho desarrollo. Simplemente se centra en la aplicación del proceso RUP para el desarrollo. No se referencian los textos o artículos utilizados para definiciones o aseveraciones realizadas, por ejemplo, sobre RUP "....es enfocada hacia "diagramas de los casos de uso, y manejo de los riesgos y el manejo de la arquitectura" como tal "   Si está entre cremillas, supongo será una copia textual de alguna parte que no se indica No se presenta un análisis profundo de la estrategia de solución ¿cuál es la lógica detrás de la aplicación móvil? Se observan algunos problemas en la redacción en el texto, por ejemplo, "...su objetivo es asegurar la producción de software de alta y mayor calidad...." La estructura del artículo tiene algunas deficiencias La figura 1 no aporta información al trabajo realizado, no es necesaria. La figura 2 no se distingue bien, además está tomada del libro de UML de Larman y no aparece la referencia en el texto. Las conclusiones del trabajo son extremadamente básicas

SOLUTION: es

PROBLEM: •I recommend to change the paper title in order to be according to paper‘ body •Review the English grammar, some but not all of the errors founded in the review are:  •Abstract   o what does it mean that for them?--> what does it mean that for them?   o …utilized to represent its appropriateness.--> …utilized to represent their appropriateness.   •Introducction   o …has like first phase… --> ?   o auscultating --> is it a verb?   •Background   o A systematic mapping study is a methodology that is used --> A systematic mapping study is a methodology used   o …of existing research on performance evaluation… -->…of existing research on the performance evaluation…   o Only Dieste and Juristo’s work [10] deals --> Only the work of Dieste and Juristo [10] deals   •Methodology   o Review the use of –ly with the verb to be   o Third question: What kind of studies does use with a higher frequency certain constructs for   o appropriateness of techniques? --> ? review the types question structure   •Classification schema   o In order to answer the research questions, the literature review focused --> literature or mapping?   •Analysis of the result   o in Figure 1 en DCM format -->in Figure 1 en DCM format.   o article --> paper   •Conclusions and future works   o For this reason, it is necessary that more proposals are carried out to help --> For this reason, it is necessary to carry out more proposals to help   •Figures: be careful on the figure´s title, the title must be following the figure  •Type of studies  •Paragraph after figure 5: Experimental (paragraph) o experiment (figure)  •Techniques considered: I recommend performing a mapping between domains and techniques and or with the constructors and show the a graphic of the results instead of the figure 8

SOLUTION: en

PROBLEM: This paper introduces a simulator and a set of synthetic tasks for evaluating a dialogue agent's ability to learn from user feedback. For solving these tasks, the paper uses memory networks (Sukhbaatar et al., 2015) learned through previously proposed supervised learning and reinforcement learning methods. In this setup, it is demonstrated that the agent learning from feedback (e.g. through question asking or question clarification) performs better.  The motivation for the paper is excellent; dialogue agents which learn directly from unstructured human feedback (as opposed to reward signals alone) could be very useful in real-world applications. However, the paper falls short on the execution. All the numerous experiments presented are based on the synthetic dialogue simulator, which is highly artificial and different from real-world dialogues. The simulator is based on a simple factoid question-answering framework, which normally is not considered dialogue and which appears to be solvable with a few hand-crafted rules. The framework also assumes that the user's feedback is always correct and is given in one of a handful of forms (e.g. paraphrase of original question without typos) and that the agent can learn from examples of another agent asking questions or making clarifications, which simplifies the task even further.  Because of the artificial setting and limited scope of the experiments, it seems difficult to draw conclusions about how to learn from unstructured user feedback. To test the hypothesis that it is possible to learn from such user feedback, I would strongly recommend the authors to continue working on this project by carrying out experiments with real human users (even in the factoid question answering domain, if necessary). This would provide much stronger evidence that a dialogue agent can learn from such feedback.   Other comments: - The abstract uses the phrase "interactive dialogue agents". What is meant by "interactive" dialogue agents? All dialogue agents interact with the user, so isn't it redundant to call them interactive? - A major limitation of the experiments is that the questions the agent can ask are specified a priori. If I understand correctly, in the supervised learning setting the agent is trained to imitate the questions of another rule-based agent. While in the RL setting, the paper states "For each dialogue, the bot takes two sequential actions $(a_1 , a_2)$: to ask or not to ask a question (denoted as a_1 ); and guessing the final answer (denoted as a_2)". This means the agent learns *when* to ask questions but not *what* questions to ask. - Related to the previous comment, in the sub-section "ONLINE REINFORCEMENT LEARNING (RL)" the paper states "We also explored scenarios where the student learns the ability to decide when to ask a question and what to ask.". Please clarify this by removing the part "what to ask". - The paper presents an overwhelming amount of results. I understand the benefit of synthetic tasks is precisely the ability to measure many aspects of model performance, but in this case it confuses the reader to present so many results. For example, what was the reason for including the "TrainAQ(+FP)" and "TrainMix" training settings? How do these results help validate the original hypothesis? If they don't, they should be taken out or moved to the appendix. - Since the contribution of the paper lies in the tasks and evaluation, it might be better to move either the vanilla-MemN2N (Table 2) to the appendix or to move the Cont-MemN2N results (Table 3) to the appendix.  --- UPDATE ---  Following the discussion below and the additional experiments provided by the authors, I have increased my score to 8.

SOLUTION:
en