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].

-	El tema de adopción de sistemas ERP se ha desarrollado los últimos 15 años, y en particular la revisión de factores que impliquen su éxito es un tema de investigación relevante. -	En el trabajo se desarrolló un análisis de la literatura ERP sobre el tema que aborda   factores críticos de éxito, metodologías de adopción y tamaño de la organización. -	Si bien es un error indicar que "últimamente se  ha generado un tercer tipo..." en relación a asociar fases del proyecto y FCE, pues hace más de 12 años esto se ha realizado, ver Cita [36] del mismo trabajo, no es un tema cerrado. -	Propongo a los autores el definir que es "éxito de adopción ERP", pues existe literatura en relación a ello muy interesante para sus fines últimos (ver trabajos de la profesora Markus). -	Metodológicamente existen tres limitaciones importantes a superar con miras a avanzar en una publicación científica de impacto. Primero, no se ha considerado el factor de impacto de la publicaciones, con lo cual se ha desestimado el desarrollo asociado a este tipo de revisiones, mezclando trabajos de alto impacto (ejemplo, artículos de revistas ISIs factor de impacto >2, como MISQ) con trabajos sin una revisión de calidad (ejemplo, lo que Scholar "lanza" sólo por coincidir en la búsqueda). Segundo, se dice que se tomarán referencias del 2008 en adelante prioritariamente, pero muchas de sus referencias  son de antes de esa fecha, y eso no es raro, pues entre 2001 y 2004 hay una explosión de trabajos en este ámbito específico. Tercero, no se han incluido explícitamente trabajos de conferencias del área de alto impacto en la comunidad científica como AMCIS o ECIS (la primera tiene una base de datos privada, no accesible por google y la segunda es libre). -	Las limitaciones antes expuestas restringen los alcances de los interesantes  resultados expuestos en el trabajo.
es

- Strengths:  - this article puts two fields together: text readability for humans and machine comprehension of texts  - Weaknesses:  - The goal of your paper is not entirely clear. I had to read the paper 4 times and I still do not understand what you are talking about! - The article is highly ambiguous what it talks about - machine comprehension or text readability for humans - you miss important work in the readability field - Section 2.2. has completely unrelated discussion of theoretical topics. - I have the feeling that this paper is trying to answer too many questions in the same time, by this making itself quite weak. Questions such as “does text readability have impact on RC datasets” should be analyzed separately from all these prerequisite skills.  - General Discussion:  - The title is a bit ambiguous, it would be good to clarify that you are referring to machine comprehension of text, and not human reading comprehension, because “reading comprehension” and “readability” usually mean that. - You say that your “dataset analysis suggested that the readability of RC datasets does not directly affect the question difficulty”, but this depends on the method/features used for answer detection, e.g. if you use POS/dependency parse features. - You need to proofread the English of your paper, there are some important omissions, like “the question is easy to solve simply look..” on page 1. - How do you annotate datasets with “metrics”?? - Here you are mixing machine reading comprehension of texts and human reading comprehension of texts, which, although somewhat similar, are also quite different, and also large areas. - “readability of text” is not “difficulty of reading contents”. Check this: DuBay, W.H. 2004. The Principles of Readability. Costa Mesa, CA: Impact information.  - it would be good if you put more pointers distinguishing your work from readability of questions for humans, because this article is highly ambiguous. E.g. on page 1 “These two examples show that the readability of the text does not necessarily correlate with the difficulty of the questions” you should add “for machine comprehension” - Section 3.1. - Again: are you referring to such skills for humans or for machines? If for machines, why are you citing papers for humans, and how sure are you they are referring to machines too? - How many questions the annotators had to annotate? Were the annotators clear they annotate the questions keeping in mind machines and not people?
en

This paper shows that a deep RL approach augmented with auxiliary tasks improves performance on navigation in complex environments. Specifically, A3C is used for the RL problem, and the agent is simultaneously trained on an unsupervised depth prediction task and a self-supervised loop closure classification task. While the use of auxiliary tasks to improve training of models including RL agents is not new, the main contribution here is the use of tasks that encourage learning an intrinsic representation of space and movement that enables significant improvements on maze navigation tasks.  The paper is well written, experiments are convincing, and the value of the auxiliary tasks for the problem are clear. However, the contribution is relatively incremental given previous work on RL for navigation and on auxiliary tasks. The work could become of greater interest provided broader analysis and insights on either optimal combinations of tasks for visual navigation (e.g. the value of other visual / geometry-based tasks), or on auxiliary tasks with RL in general.  As it is, it is a useful demonstration of the benefit of geometry-based auxiliary tasks for navigation, but of relatively narrow interest.

en