Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer
Computation and Language
2021-07-06 v2
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.
Cite
@article{arxiv.2105.06947,
title = {Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer},
author = {Huiyuan Lai and Antonio Toral and Malvina Nissim},
journal= {arXiv preprint arXiv:2105.06947},
year = {2021}
}