Authorship Style Transfer with Policy Optimization
<|reference_start|>Authorship Style Transfer with Policy Optimization: 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.<|reference_end|>
@article{liu2024authorship,
    title={Authorship Style Transfer with Policy Optimization},
    author={Shuai Liu, Shantanu Agarwal, Jonathan May},
    journal={arXiv preprint arXiv:2403.08043},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08043},
    primaryClass={cs.CL}
}