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].

[Q]: This nicely written paper presents an end-to-end learning method for image compression. By optimizing for rate-distortion performance and a clever relaxation the method is able to learn an efficient image compression method by optimizing over a database of natural images.  As the method is interesting, results are interesting and analysis is quite thorough it's easy for me to recommend acceptance.
[A]: en


[Q]: El artículo está muy bien escrito y se lee con facilidad, teniendo básicamente características de un artículo de divulgación.  Se discuten aspectos centrados esencialmente en los problemas de recuperación de información ante el crecimiento exponencial de los datos. La propuesta realizada al final del artículo es interesante, aunque, desde mi punto de vista la inclusión del tema Big Data está dado con cierta liviandad y requiere un análisis más detallado de lo que se describe.
[A]: es


[Q]: The authors describe a dataset of proof steps in higher order logic derived from a set of proven theorems. The success of methods like AlphaGo suggests that for hard combinatorial style problems, having a curated set of expert data (in this case the sequence of subproofs) is a good launching point for possibly super-human performance. Super-human ATPs are clearly extremely valuable. Although relatively smaller than the original Go datasets, this dataset seems to be a great first step. Unfortunately, the ATP and HOL aspect of this work is not my area of expertise. I can't comment on the quality of this aspect.  It would be great to see future work scale up the baselines and integrate the networks into state of the art ATPs. The capacity of deep learning methods to scale and take advantage of larger datasets means there's a possibility of an iterative approach to improving ATPs: as the ATPs get stronger they may generate more data in the form of new theorems. This may be a long way off, but the possibility is exciting.
[A]:
en