  Text Style Transfer (TST) aims to alter the underlying style of the source
text to another specific style while keeping the same content. Due to the
scarcity of high-quality parallel training data, unsupervised learning has
become a trending direction for TST tasks. In this paper, we propose a novel
VAE based Text Style Transfer with pivOt Words Enhancement leaRning (VT-STOWER)
method which utilizes Variational AutoEncoder (VAE) and external style
embeddings to learn semantics and style distribution jointly. Additionally, we
introduce pivot words learning, which is applied to learn decisive words for a
specific style and thereby further improve the overall performance of the style
transfer. The proposed VT-STOWER can be scaled to different TST scenarios given
very limited and non-parallel training data with a novel and flexible style
strength control mechanism. Experiments demonstrate that the VT-STOWER
outperforms the state-of-the-art on sentiment, formality, and code-switching
TST tasks.
