  Style transfer is the task of rewriting a sentence into a target style while
approximately preserving content. While most prior literature assumes access to
a large style-labelled corpus, recent work (Riley et al. 2021) has attempted
"few-shot" style transfer using only 3-10 sentences at inference for style
extraction. In this work we study a relevant low-resource setting: style
transfer for languages where no style-labelled corpora are available. We notice
that existing few-shot methods perform this task poorly, often copying inputs
verbatim. We push the state-of-the-art for few-shot style transfer with a new
method modeling the stylistic difference between paraphrases. When compared to
prior work, our model achieves 2-3x better performance in formality transfer
and code-mixing addition across seven languages. Moreover, our method is better
at controlling the style transfer magnitude using an input scalar knob. We
report promising qualitative results for several attribute transfer tasks
(sentiment transfer, simplification, gender neutralization, text anonymization)
all without retraining the model. Finally, we find model evaluation to be
difficult due to the lack of datasets and metrics for many languages. To
facilitate future research we crowdsource formality annotations for 4000
sentence pairs in four Indic languages, and use this data to design our
automatic evaluations.
