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:
While I understand the difficulty of collecting audio data from animals, I think this type of feature engineering does not go in the right direction. I would rather see a model than learns the feature representation from data.  I would think it should be possible to collect a more substantial corpus in zoos / nature etc, and then train a generative model. The underlying learned feature representation could be then used to feed a classifier. I'm not familiar with the particularities of this task, it's hard to judge the improvements by using chirplets.

Ex Output:
en


Ex Input:
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.

Ex Output:
en


Ex Input:
El artículo presenta un sistema de información comercial adaptable a cualquier negocio minorista a partir de facilitar el trabajo de consolidación de los datos y completar la automatización del proceso de importación, distribución y aprovisionamiento.  A pesar de buena presentación y lenguaje, no es un trabajo que presente resultados de una investigación. Más bien es el resumen del desarrollo de un sistema de mediana complejidad. Tampoco expone lecciones aprendidas o prácticas que pudieran aportar a otros desarrolladores. En conclusión, su novedad y contribución a la ciencia es limitada. En estas condiciones, creo que no puede aceptarse para el track de investigación.

Ex Output:

es