  Generating from Abstract Meaning Representation (AMR) is an underspecified
problem, as many syntactic decisions are not constrained by the semantic graph.
To explicitly account for this underspecification, we break down generating
from AMR into two steps: first generate a syntactic structure, and then
generate the surface form. We show that decomposing the generation process this
way leads to state-of-the-art single model performance generating from AMR
without additional unlabelled data. We also demonstrate that we can generate
meaning-preserving syntactic paraphrases of the same AMR graph, as judged by
humans.
