<cite>Bastings et al. (2017)</cite> used Graph Convolutional Networks (GCNs) to encode syntactic structure. GCNs were originally proposed by Kipf and Welling (2016) and modified to handle labeled and automatically predicted (hence noisy) syntactic dependency graphs by . Representations of nodes (i.e. words in a sentence) in GCNs are directly influenced by representations of their neighbors in the graph. The form of influence (e.g., transition matrices and parameters of gates) are learned in such a way as to benefit the end task (i.e. translation). These linguistically-aware word representations are used within a neural encoder.