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:  The authors present a novel adaptation of encoder-decoder neural MT using an approach that starts and ends with characters, but in between works with representations of morphemes and characters.   The authors release both their code as well as their final learned models for fr-en, cs-en, and en-cs. This is helpful in validating their work, as well as for others looking to replicate and extends this work.  The system reported appears to produce translation results of reasonable quality even after the first training epoch, with continued progress in future epochs.  The system appears to learn reasonable morphological tokenizations, and appears able to handle previously unseen words (even nonce words) by implicitly backing off to morphemes.  - Weaknesses:  In the paper, the authors do not explicitly state which WMT test and dev sets their results are reported on. This is problematic for readers wishing to compare the reported results to existing work (for example, the results at matrix.statmt.org). The only way this reviewer found to get this information was to look in the README of the code supplement, which indicates that the test set was newstest2015 and the dev test was newstest2013. This should have been explicitly described in the paper.  The instructions given in the software README are OK, but not great. The training and testing sections each could be enhanced with explicit examples of how to run the respective commands. The software itself should respond to a --help flag, which it currently does not.  The paper describes a 6-level architecture, but the diagram in Figure 2 appears to show fewer than 6 layers. What's going on? The caption should be more explicit, and if this figure is not showing all of the layers, then there should be a figure somewhere (even if it's in an appendix) showing all of the layers.  The results show comparison to other character-based neural systems, but do not show state-of-the-art results for other types of MT system. WMT (and matrix.statmt.org) has reported results for other systems on these datasets, and it appears that the state-of-the-art is much higher than any of the results reported in this paper. That should be acknowledged, and ideally should be discussed.  There are a handful of minor English disfluencies, misspellings, and minor LaTeX issues, such as reverse quotation marks. These should be corrected.  - General Discussion:  Paper is a nice contribution to the existing literature on character-based neural MT.
en

The authors explore the idea of deep-learning a static analyzer. They do it with a toy programming language and a very simplified analysis problem -- just checking if all variables are initalized.  While the idea is interesting and might be developped into a tool in the future, the toy task presented in this paper is too simple to warrant an ICLR submission. Just detecting whether a variable is initialized in a string is a toy algorihtmic task, similar to the ones solved in a number of paper in recent years by models such as the Neural Turing Machine, Stack RNNs, Neural GPU, or Differentiable Neural Computer. All these architectures perform almost perfectly on a number of algorithmic tasks, so it is highly probable that they would also solve this one. Unluckily, the authors only compare to much more basic models, such as HMMs. Since the code for many of the above-mentioned models is available online, a paper without these baselines is not ready for ICLR. Moreover, there is a risk that existing models already solve this problem very well, making the contribution unclear.

en