  Text generation from AMR involves emitting sentences that reflect the meaning
of their AMR annotations. Neural sequence-to-sequence models have successfully
been used to decode strings from flattened graphs (e.g., using depth-first or
random traversal). Such models often rely on attention-based decoders to map
AMR node to English token sequences. Instead of linearizing AMR, we directly
encode its graph structure and delegate traversal to the decoder. To enforce a
sentence-aligned graph traversal and provide local graph context, we predict
transition-based parser actions in addition to English words. We present two
model variants: one generates parser actions prior to words, while the other
interleaves actions with words.
