LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer
Machel Reid, Victor W. Zhong · 2021
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.