Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations
Xiaoyan Li, Sun Sun, Yunli Wang · 2021
Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content.Most previous work adopt disentangled latent representation learning to realize style transfer.We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style.Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer.Extensive experimental results show that our method generally outperforms state-of-theart approaches.