Abstract
Text style transfer (TST) involves transforming a text into a desired style while approximately preserving its content. The biggest challenge in TST in the general lack of parallel data. Many existing approaches rely on complex models using substantial non-parallel data, with mixed results. In this paper, we leverage a pretrained BART language model with minimal parallel data and incorporate low-resource methods such as hyperparameter tuning, data augmentation, and self-training, which have not been explored in TST. We further include novel style-based rewards in the training loss. Through extensive experiments in sentiment transfer, a sub-task of TST, we demonstrate that our simple yet effective approaches achieve well-balanced results, surpassing non-parallel approaches and highlighting the usefulness of parallel data even in small amounts.- Anthology ID:
- 2023.inlg-main.27
- Volume:
- Proceedings of the 16th International Natural Language Generation Conference
- Month:
- September
- Year:
- 2023
- Address:
- Prague, Czechia
- Editors:
- C. Maria Keet, Hung-Yi Lee, Sina Zarrieß
- Venues:
- INLG | SIGDIAL
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 388–395
- Language:
- URL:
- https://aclanthology.org/2023.inlg-main.27
- DOI:
- 10.18653/v1/2023.inlg-main.27
- Cite (ACL):
- Sourabrata Mukherjee and Ondrej Dusek. 2023. Leveraging Low-resource Parallel Data for Text Style Transfer. In Proceedings of the 16th International Natural Language Generation Conference, pages 388–395, Prague, Czechia. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Low-resource Parallel Data for Text Style Transfer (Mukherjee & Dusek, INLG-SIGDIAL 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.inlg-main.27.pdf