Prototype editing extracts a sentence template and its attribute markers to generate the text. 

Hamilton College

With the success of deep learning in the last decade, a variety of neural methods have been recently proposed for TST. If parallel data are provided, standard sequence-to-sequence models are often directly applied. However, most use cases do not have parallel data, so TST on non-parallel corpora has
become a prolific research area

Approaches of TST

Jin, Di, et al. “Deep Learning for Text Style Transfer: A Survey.” ACL Anthology, https://aclanthology.org/2022.cl-1.6/. 

Deep Learning for Text Style Transfer: A Survey

Prototype Editing

The first line of approaches disentangle text into its content and attribute in the latent space, and apply generative modeling.

Disentanglement

Pseudo-parallel corpus construction trains the model as if in a supervised way with the pseudo-parallel data.

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