  We motivate and propose a suite of simple but effective improvements for
concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc
PHrase Infilling and REcombination. We demonstrate their effectiveness on
generative commonsense reasoning, a.k.a. the CommonGen task, through
experiments using both BART and T5 models. Through extensive automatic and
human evaluation, we show that SAPPHIRE noticeably improves model performance.
An in-depth qualitative analysis illustrates that SAPPHIRE effectively
addresses many issues of the baseline model generations, including lack of
commonsense, insufficient specificity, and poor fluency.
