A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer

Understand

Unsupervised text style transfer aims to transfer the underlying style of text but keep its main content unchanged without parallel data.

• Most existing methods typically follow two steps: first separating the content from the original style, and then fusing the content with the desired style.
• However, the separation in the first step is challenging because the content and style interact in subtle ways in natural language.
• Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.