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].

Comentarios del artículo. De forma: •	Problemas con el uso de mayúscula inicial. •	Desconformidad? Sería mejor inconformidad •	Problemas de ortografía. Faltan tildes en algunas palabras (por ejemplo: implantará), y sobran en otras (critérios). No tiene mucha presentación este tipo de errores teniendo en cuenta que los procesadores de texto cuentan con corrector de ortografía. •	Artículos en español deben escribirse siempre en tercera persona. •	Problemas con el sistema de referenciación.    De fondo: •	Las figuras 1 y 2 no aportan al entendimiento del problema y la solución. Favor insertar solo figuras que permitan una mejor comprensión de la necesidad. •	El orden para presentar la información es confuso. Primero debería abordarse la sección de teoría y de trabajos previos para tener el background necesario y así entender porque es necesaria la construcción de una nueva aplicación. •	En la introducción siempre el último párrafo debe dar cuenta de la estructura del artículo. Falta esto para que el lector tenga una visión general del paper. •	En la sección teoría del dominio mencionan la definición de CRM, pero al parecer lo que ofrecen como solución no es una aplicación de este tipo. Esto genera confusión en la lectura. Igualmente parece que están mezclando las definiciones con la sección donde comparan otras aplicaciones, o cual tampoco favorece el entendimiento del artículo. •	Al indicar las aplicaciones comparadas no queda muy claro qué criterios se usaron para su selección. Qué se revisó en cuanto a la literatura especializada? •	La calidad de la figura 3 no es adecuada para una publicación. •	La figura 4 y su etiqueta explicativa están en diferentes posiciones. Dicha figura también tiene problemas de calidad. •	La forma de mostrar la solución propuesta es muy confusa. Vale la pena reorganizar toda la sección para explicar primero el proceso propuesto en el desarrollo y posteriormente los resultados obtenidos. •	Teniendo en cuenta el objetivo del artículo, una sección de modelo de indicadores de la extensión usada le roba espacio a aspectos más importantes a tratar como por ejemplo la utilidad de que la aplicación sea móvil y cuente con datos como la ubicación del vendedor. •	El título del trabajo no se compadece del contenido. No se hizo énfasis en el componente móvil que aparentemente era la mayor bondad de la aplicación. •	La sección de estadísticas no conecta con el resto del contenido, y tampoco se usan leyendas explicativas para lograr esta conexión.
es

Summary: The paper proposes a large-scale dataset for reading comprehension, with the final goal of releasing 1 million questions and answers. The authors have currently released 100,000 queries and their answers. The dataset differs from existing reading comprehension datasets mainly w.r.t queries being sampled from user queries rather than being generated by crowd-workers and answers being generated by crowd-workers rather than being spans of text from the provided passage. The paper presents some analysis of the dataset such as distribution of answer types. The paper also presents the results of some generative and some cloze-style models on the MS MARCO dataset.  Strengths:  1. The paper provides useful insights about the limitations of the existing reading comprehension datasets – questions asked by crowd-workers have different distribution compared to that of questions asked by actual users of intelligent agents, answers being restricted to span from the reading text rather than requiring reasoning across multiple pieces of text/passages.  2. MS MARCO dataset has novel useful characteristics compared to existing reading comprehension datasets – questions are sampled from user queries, answers are generated by humans.  3. The experimental evaluation of the existing baseline models on the MS MARCO dataset is satisfactory.  Weaknesses/Suggestions:  1. The paper does not report human performance on the dataset. Human performance should be reported to estimate the difficulty of the dataset. The degree of inter-human agreement will also reflect how well the metric (being used to compute inter-human agreement and accuracies of the baseline models) can deal with variance in the sentence structure with similar semantics.  2. I would like to see the comparison between the answer type distribution in the MS MARCO dataset and that in existing reading comprehension datasets such as SQuAD. This would ground the claim made in the paper the distributions of questions asked by crowd-workers is different from that of user queries.  3. The paper uses automatic metrics such as ROUGE, BLEU for evaluating natural language answers. However, it is known that such metrics poorly correlate with human judgement for tasks such as image caption evaluation (Chen et al., Microsoft COCO Captions: Data Collection and Evaluation Server, CoRR abs/1504.00325 (2015)). So, I wonder how authors justify using such metrics for evaluating open-ended natural language answers.  4. The paper mentions that a classifier was used to filter answer seeking queries from all Bing queries. It would be good to mention the accuracy of this classifier. This will provide insights into what percentage of the MS MARCO questions are answer seeking queries. Similarly, what is the accuracy of the information retrieval based system used to retrieve passages for filtered queries?  5. Please include the description of the best passage baseline in the paper.    6. Fix opening quotes, i.e. ” -> “ (for instance, on page 5, ”what” -> “what”).  Review Summary: The paper is well motivated, the use of user queries and human generated answers makes the dataset different from existing datasets. However, I would like to see the human performance on the dataset and quantitative comparison between the distribution of questions obtained from user queries and that of crowd-sourced questions. I would also like the authors to comment on the use of automatic metrics (such as ROUGE, BLEU) in the light of the fact that such metrics do not correlate well with human judgements for tasks such as image caption evaluation.
en

This paper proposed to use RL and RNN to design the architecture of networks for specific tasks. The idea of the paper is quite promising and the experimental results on two datasets show that method is solid. The pros of the paper are: 1. The idea of using RNN to produce the description of the network and using RL to train the RNN is interesting and promising. 2. The generated architecture looks similar to what human designed, which shows that the human expertise and the generated network architectures are compatible.  The cons of the paper are: 1. The training time of the network is long, even with a lot of computing resources.  2. The experiments did not provide the generality of the generated architectures. It would be nice to see the performances of the generated architecture on other similar but different datasets, especially the generated sequential models.  Overall, I believe this is a nice paper. But it need more experiments to show its potential advantage over the human designed models.

en