Graph neural networks are a family of neural architectures (Scarselli et al., 2009; Gilmer et al., 2017) specifically devised to induce representation of nodes in a graph relying on its graph structure. Graph convolutional networks (GCNs) belong to this family. While GCNs were introduced BiRNN CNN Baseline<cite> (Bastings et al., 2017)</cite> 14.9 12.6 +Sem 15.6 13.4 +Syn<cite> (Bastings et al., 2017)</cite> 16.1 13.7 +Syn + Sem 15.8 14.3 for modeling undirected unlabeled graphs (Kipf and Welling, 2016) , in this paper we use a formulation of GCNs for labeled directed graphs, where the direction and the label of an edge are incorporated.