  AMR-to-text generation is a problem recently introduced to the NLP community,
in which the goal is to generate sentences from Abstract Meaning Representation
(AMR) graphs. Sequence-to-sequence models can be used to this end by converting
the AMR graphs to strings. Approaching the problem while working directly with
graphs requires the use of graph-to-sequence models that encode the AMR graph
into a vector representation. Such encoding has been shown to be beneficial in
the past, and unlike sequential encoding, it allows us to explicitly capture
reentrant structures in the AMR graphs. We investigate the extent to which
reentrancies (nodes with multiple parents) have an impact on AMR-to-text
generation by comparing graph encoders to tree encoders, where reentrancies are
not preserved. We show that improvements in the treatment of reentrancies and
long-range dependencies contribute to higher overall scores for graph encoders.
Our best model achieves 24.40 BLEU on LDC2015E86, outperforming the state of
the art by 1.1 points and 24.54 BLEU on LDC2017T10, outperforming the state of
the art by 1.24 points.
