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.

本文提出了一种从粗到细的编辑器来进行风格转移，该编辑器使用 Levenshtein 编辑操作同时编辑源文本中的多个范围；为了训练，提出了一个无监督的数据合成过程，并使用 fine-tuned 预训练语言模型填充模板。该方法在情感（Yelp，Amazon）和礼貌（Polite）转换中优于现有的生成和编辑风格转移方法。与单范围编辑相比，多范围编辑实现了更高的性能和更多样化的输出。与以往的无监督数据综合方法相比，该方法产生了更高质量的并行样式对，并改善了模型性能。