Authorship style transfer aims to rewrite a given text into a specified target while preserving the original meaning in the source. Existing approaches rely on the availability of a large number of target style exemplars for model training. However, these overlook cases where a limited number of target style examples are available. The development of parameter-efficient transfer learning techniques and policy optimization (PO) approaches suggest lightweight PO is a feasible approach to low-resource style transfer. In this work, we propose a simple two-stage tune-and-optimize technique for low-resource textual style transfer. We apply our technique to authorship transfer as well as a larger-data native language style task and in both cases find it outperforms state-of-the-art baseline models.
@article{arxiv.2403.08043,
title = {Authorship Style Transfer with Policy Optimization},
author = {Shuai Liu and Shantanu Agarwal and Jonathan May},
journal= {arXiv preprint arXiv:2403.08043},
year = {2024}
}