  The problem of AMR-to-text generation is to recover a text representing the
same meaning as an input AMR graph. The current state-of-the-art method uses a
sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR
structure. Although being able to model non-local semantic information, a
sequence LSTM can lose information from the AMR graph structure, and thus faces
challenges with large graphs, which result in long sequences. We introduce a
neural graph-to-sequence model, using a novel LSTM structure for directly
encoding graph-level semantics. On a standard benchmark, our model shows
superior results to existing methods in the literature.
