  The Surface Realization Shared Tasks of 2018 and 2019 were Natural Language
Generation shared tasks with the goal of exploring approaches to surface
realization from Universal-Dependency-like trees to surface strings for several
languages. In the 2018 shared task there was very little difference in the
absolute performance of systems trained with and without additional,
synthetically created data, and a new rule prohibiting the use of synthetic
data was introduced for the 2019 shared task. Contrary to the findings of the
2018 shared task, we show, in experiments on the English 2018 dataset, that the
use of synthetic data can have a substantial positive effect - an improvement
of almost 8 BLEU points for a previously state-of-the-art system. We analyse
the effects of synthetic data, and we argue that its use should be encouraged
rather than prohibited so that future research efforts continue to explore
systems that can take advantage of such data.
