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].

[EX Q]: En  Cap III no veo diferencias o aportes  sustantivos a lo ya manifestado en documento “la necesidad en Chile de una infraestructura tecnológica colaborativa de apoyo a la investigación astronómica” Red Universitaria Nacional, Noviembre 2008. De manera que corresponde citarlo como referencia o sino hacer los aportes o diferencias sustantivas, al igual que [Link]  Finalmente, el artículo carece de relevancia, ya que los aportes no se diferencias de lo que presenta.   Más aún, las figuras no se explican, como tampoco se cita su fuente que están dadas en [Link]   No cabe duda que lo relevante serán los resultados que se obtengan del  proyecto (FONDEF D11I1060, adjudicado el año 2011), en el sentido del diseño e implementación de un observatorio virtual, el cual deberá cumplir con los estándares de IVOA, sin embargo, por ahora, no veo aportes sustantivos.  Además, no se mencionan las "instancias donde presentarán problemáticas", tampoco la o las "problemáticas que enfrentan" como comunidad ante el procesamiento de datos que serán resueltos mediante "técnicas computacionales", las que hasta ahora son desconocidas.  Por otro lado la arquitectura que se muestra y me parece natural es la de IVOA, sin embargo, no se muestran, al menos por ahora, los aportes que se han realizado en este sentido.
[EX A]: es

[EX Q]: Authors propose a competitive learning architecture that learn different RNN predictors independently, akin to a committee of experts which are chosen with a hard switch at run-time. This work is applied to the task of predictive different driving behaviors from human drivers, and combines behaviors at test time, often switching behaviors within seconds. Prediction loss is lower than the similar but non-competitive architecture used as a baseline. It is not very clear how to interpret the results, what is the real impact of the model. If behaviors switch very often, can this really be seen as choosing the best driving mode for a given situation? Maybe the motivation needs to be rephrased a little to be more convincing? The competitive approach presented is interesting but not really novel, thus the impact of this paper for a conference such as ICLR may be limited.
[EX A]: en

[EX Q]: - Strengths: The authors have nice coverage of a different range of language settings to isolate the way that relatedness and amount of morphology interact (i.e., translating between closely related morphologically rich languages vs distant ones) in affecting what the system learns about morphology. They include an illuminating analysis of what parts of the architecture end up being responsible for learning morphology, particularly in examining how the attention mechanism leads to more impoverished target side representations. Their findings are of high interest and practical usefulness for other users of NMT.   - Weaknesses: They gloss over the details of their character-based encoder. There are many different ways to learn character-based representations, and omitting a discussion of how they do this leaves open questions about the generality of their findings. Also, their analysis could've been made more interesting had they chosen languages with richer and more challenging morphology such as Turkish or Finnish, accompanied by finer-grained morphology prediction and analysis.  - General Discussion: This paper brings insight into what NMT models learn about morphology by training NMT systems and using the encoder or decoder representations, respectively, as input feature representations to a POS- or morphology-tagging classification task. This paper is a straightforward extension of "Does String-Based Neural MT Learn Source Syntax?," using the same methodology but this time applied to morphology. Their findings offer useful insights into what NMT systems learn.
[EX A]:
en