Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer

Huiyuan Lai; Antonio Toral; Malvina Nissim

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arXiv · 2105.06947

Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer

Huiyuan Lai·Antonio Toral·Malvina Nissim

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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.

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Huiyuan Lai, Antonio Toral, Malvina Nissim. 2021-05-14. Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer. https://arxiv.org/abs/2105.06947

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