In this work we propose a model for graph-tosequence (henceforth, g2s) learning that leverages recent advances in neural encoder-decoder architectures.
we employ an encoder based on Gated Graph Neural Networks (Li et al., 2016, GGNNs), which can incorporate the full graph structure without loss of information.