  Many types of text style transfer can be achieved with only small, precise
edits (e.g. sentiment transfer from I had a terrible time... to I had a great
time...). We propose a coarse-to-fine editor for style transfer that transforms
text using Levenshtein edit operations (e.g. insert, replace, delete). Unlike
prior single-span edit methods, our method concurrently edits multiple spans in
the source text. To train without parallel style text pairs (e.g. pairs of +/-
sentiment statements), we propose an unsupervised data synthesis procedure. We
first convert text to style-agnostic templates using style classifier attention
(e.g. I had a SLOT time...), then fill in slots in these templates using
fine-tuned pretrained language models. Our method outperforms existing
generation and editing style transfer methods on sentiment (Yelp, Amazon) and
politeness (Polite) transfer. In particular, multi-span editing achieves higher
performance and more diverse output than single-span editing. Moreover,
compared to previous methods on unsupervised data synthesis, our method results
in higher quality parallel style pairs and improves model performance.
