  We propose neural models to generate high-quality text from structured
representations based on Minimal Recursion Semantics (MRS). MRS is a rich
semantic representation that encodes more precise semantic detail than other
representations such as Abstract Meaning Representation (AMR). We show that a
sequence-to-sequence model that maps a linearization of Dependency MRS, a
graph-based representation of MRS, to English text can achieve a BLEU score of
66.11 when trained on gold data. The performance can be improved further using
a high-precision, broad coverage grammar-based parser to generate a large
silver training corpus, achieving a final BLEU score of 77.17 on the full test
set, and 83.37 on the subset of test data most closely matching the silver data
domain. Our results suggest that MRS-based representations are a good choice
for applications that need both structured semantics and the ability to produce
natural language text as output.
