MSSRNet: Manipulating Sequential Style Representation for Unsupervised
  Text Style Transfer
Yazheng Yang Zhou Zhao Qi Liu
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.