Task: Language Identification
Input (Spanish, English): The paper proposes a model that aims at learning to label nodes of graph in a semi-supervised setting. The idea of the model is based on the use of the graph structure to regularize the representations learned at the node levels. Experimental results are provided on different tasks  The underlying idea of this paper (graph regularization) has been already explored in different papers – e.g 'Learning latent representations of nodes for classifying in heterogeneous social networks' [Jacob et al. 2014],   [Weston et al 2012] where a real graph structure is used instead of a built one. The experiments lack of strong comparisons with other graph models (e.g Iterative Classification, 'Learning from labeled and unlabeled data on a directed graph', ...). So the novelty of the paper and the experimental protocol are not strong enough to accpet the paper.  Pros: * Learning over graph is an important topic  Cons: * Many existing approaches have already exploited the same types of ideas, resulting in very close models * Lack of comparison w.r.t existing models
Output (English): en