  Text style transfer aims to paraphrase a sentence in one style into another
style while preserving content. Due to lack of parallel training data,
state-of-art methods are unsupervised and rely on large datasets that share
content. Furthermore, existing methods have been applied on very limited
categories of styles such as positive/negative and formal/informal. In this
work, we develop a meta-learning framework to transfer between any kind of text
styles, including personal writing styles that are more fine-grained, share
less content and have much smaller training data. While state-of-art models
fail in the few-shot style transfer task, our framework effectively utilizes
information from other styles to improve both language fluency and style
transfer accuracy.
