Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2105.06947