Detailed Instructions: 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].
See one example below:
Problem: 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.
Solution: es
Explanation: This review is written in spanish. Hence, the label is 'es'.

Problem: This paper proposes a character-aware attention residual network for sentence embedding. Several text classification tasks are used to evaluate the effectiveness of the proposed model. On two of the three tasks, the residual network outforms a few baselines, but couldn't beat the simple TFIDF-SVM on the last one. This work is not novel enough. Character information has been applied in many previously published work, as cited by the authors. Residual network is also not new. Why not testing the model on a few more widely used datasets for short text classification, such as TREC? More competitive baselines can be compared to. Also, it's not clear how the "Question" dataset was created and which domain it is. Last, it is surprising that the format of citations throughout the paper is all wrong. For example: like Word2Vec Mikolov et al. (2013) -> like Word2Vec (Mikolov et al., 2013) The citations can't just mix with the normal text. Please refer to other published papers.
Solution:
en