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We present a novel approach to the problem of text style transfer. Unlikeprevious approaches requiring style-labeled training data, our method makes useof readily-available unlabeled text by relying on the implicit connection instyle between adjacent sentences, and uses labeled data only at inference time.We adapt T5 (Raffel et al., 2020), a strong pretrained text-to-text model, toextract a style vector from text and use it to condition the decoder to performstyle transfer. As our label-free training results in a style vector spaceencoding many facets of style, we recast transfers as "targeted restyling"vector operations that adjust specific attributes of the input while preservingothers. We demonstrate that training on unlabeled Amazon reviews data resultsin a model that is competitive on sentiment transfer, even compared to modelstrained fully on labeled data. Furthermore, applying our novel method to adiverse corpus of unlabeled web text results in a single model capable oftransferring along multiple dimensions of style (dialect, emotiveness,formality, politeness, sentiment) despite no additional training and using onlya handful of exemplars at inference time.

TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling