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].
One example is below.
Q: Este articulo no es un articulo de investigacion, ya que solo muestra como programar un robot mediante la herramienta de logica difusa. Este tema ya ha sido propuesto como solucion en navegacion de robots.
A: es
Rationale: This review is written in spanish. Hence, the label is 'es'.
Q: This paper evaluates several strategies to reduce output vocabulary size in order to speed up NMT decoding and training. It could be quite useful to practitioners, although the main contributions of the paper seem somewhat orthogonal to representation learning and neural networks, and I am not sure ICLR is the ideal venue for this work. - Do the reported decoding times take into account the vocabulary reduction step? - Aside from machine translation, might there be applications to other settings such as language modeling, where large vocabulary is also a scalability challenge? - The proposed methods are helpful because of the difficulties induced by using a word-level model. But (at least in my opinion) starting from a character or even lower-level abstraction seems to be the obvious solution to the huge vocabulary problem.
A:
en