Conditioning on Dialog Acts improves Empathy Style Transfer
Renyi Qu, Lyle Ungar, João Sedoc
Abstract
We explore the role of dialog acts in style transfer, specifically empathy style transfer -- rewriting a sentence to make it more empathetic without changing its meaning. Specifically, we use two novel few-shot prompting strategies: target prompting, which only uses examples of the target style (unlike traditional prompting with source/target pairs), and dialog-act-conditioned prompting, which first estimates the dialog act of the source sentence and then makes it more empathetic using few-shot examples of the same dialog act. Our study yields two key findings: (1) Target prompting typically improves empathy more effectively while maintaining the same level of semantic similarity; (2) Dialog acts matter. Dialog-act-conditioned prompting enhances empathy while preserving both semantics and the dialog-act type. Different dialog acts benefit differently from different prompting methods, highlighting the need for further investigation of the role of dialog acts in style transfer.
BibTeX
@inproceedings{
qu2023conditioning,
title={Conditioning on Dialog Acts improves Empathy Style Transfer},
author={Renyi Qu and Lyle Ungar and Jo{\~a}o Sedoc},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=AQiuwWLvim}
}