  This work presents a new state of the art in reconstruction of surface
realizations from obfuscated text. We identify the lack of sufficient training
data as the major obstacle to training high-performing models, and solve this
issue by generating large amounts of synthetic training data. We also propose
preprocessing techniques which make the structure contained in the input
features more accessible to sequence models. Our models were ranked first on
all evaluation metrics in the English portion of the 2018 Surface Realization
shared task.
