  Scarcity of parallel data causes formality style transfer models to have
scarce success in preserving content. We show that fine-tuning pre-trained
language (GPT-2) and sequence-to-sequence (BART) models boosts content
preservation, and that this is possible even with limited amounts of parallel
data. Augmenting these models with rewards that target style and content -- the
two core aspects of the task -- we achieve a new state-of-the-art.
