Authorship Style Transfer with Policy Optimization

• URL: http://arxiv.org/abs/2403.08043v2
• Date: Sun, 28 Jul 2024 04:29:43 GMT
• Title: Authorship Style Transfer with Policy Optimization
• Authors: Shuai Liu, Shantanu Agarwal, Jonathan May,
• Abstract summary: 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.
• Score: 26.34892894935038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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