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.- Anthology ID:
- 2023.findings-emnlp.884
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13254–13271
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.884
- DOI:
- 10.18653/v1/2023.findings-emnlp.884
- Cite (ACL):
- Renyi Qu, Lyle Ungar, and João Sedoc. 2023. Conditioning on Dialog Acts improves Empathy Style Transfer. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13254–13271, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Conditioning on Dialog Acts improves Empathy Style Transfer (Qu et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-emnlp.884.pdf