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 Input:
It is an interesting idea to go after saddle points in the optimization with an SR1 update and a good start in experiments, but missing important comparisons to recent 2nd order optimizations such as Adam, other Hessian free methods (Martens 2012), Pearlmutter fast exact multiplication by the Hessian. From the mnist/cifar curves it is not really showing an advantage to AdaDelta/Nag (although this is stated), and much more experimentation is needed to make a claim about mini-batch insensitivity to performance, can you show error rates on a larger scale task?

Ex Output:
en


Ex Input:
1) Summary  This paper proposes an end-to-end hybrid architecture to predict the local linear trends of time series. A temporal convnet on raw data extracts short-term features. In parallel, long term representations are learned via a LSTM on piecewise linear approximations of the time series. Both representations are combined using a MLP with one hidden layer (in two parts, one for each stream), and the entire architecture is trained end-to-end by minimizing (using Adam) the (l2-regularized) euclidean loss w.r.t. ground truth local trend durations and slopes.   2) Contributions  + Interesting end-to-end architecture decoupling short-term and long-term representation learning in two separate streams in the first part of the architecture. + Comparison to deep and shallow baselines.  3) Suggestions for improvement  Add a LRCN baseline and discussion: The benefits of decoupling short-term and long-term representation learning need to be assessed by comparing to the popular "long-term recurrent convolutional network" (LRCN) of Donahue et al (

Ex Output:
en


Ex Input:
Hoy en día los artículos de "revisión" son de mucha utilidad, y la forma de concebirlo es toda una discusión. Por tal motivo, es relevante saber la metodología de búsqueda y recuperación de la información (por años, temas, revistas, journal, etc) para con ello, conseguir una buena redacción técnica que le permita hacer llegar a los lectores los conocimientos que se quieren transmitir.

Ex Output:

es