MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer
This work addresses the challenge of accurately conveying style strength per token in text style transfer, offering an incremental improvement over traditional fixed-vector methods.
The paper tackles the problem of unsupervised text style transfer by proposing a method that assigns individual style vectors to each token for fine-grained control, resulting in clearly improved style transfer accuracy and content preservation in both two-style and multi-style settings.
Unsupervised text style transfer task aims to rewrite a text into target style while preserving its main content. Traditional methods rely on the use of a fixed-sized vector to regulate text style, which is difficult to accurately convey the style strength for each individual token. In fact, each token of a text contains different style intensity and makes different contribution to the overall style. Our proposed method addresses this issue by assigning individual style vector to each token in a text, allowing for fine-grained control and manipulation of the style strength. Additionally, an adversarial training framework integrated with teacher-student learning is introduced to enhance training stability and reduce the complexity of high-dimensional optimization. The results of our experiments demonstrate the efficacy of our method in terms of clearly improved style transfer accuracy and content preservation in both two-style transfer and multi-style transfer settings.