Authorship Style Transfer with Policy Optimization
Shuai Liu Shantanu Agarwal Jonathan May
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 step 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.