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

• URL: http://arxiv.org/abs/2010.03802v3
• Date: Wed, 23 Jun 2021 06:16:15 GMT
• Title: TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling
• Authors: Parker Riley, Noah Constant, Mandy Guo, Girish Kumar, David Uthus, Zarana Parekh
• Abstract summary: We present a novel approach to the problem of text style transfer. Our method makes use of readily-available unlabeled text by relying on the implicit connection in style between adjacent sentences. We demonstrate that training on unlabeled Amazon reviews data results in a model that is competitive on sentiment transfer.
• Score: 23.60231661500702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel approach to the problem of text style transfer. Unlike previous approaches requiring style-labeled training data, our method makes use of readily-available unlabeled text by relying on the implicit connection in style 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, to extract a style vector from text and use it to condition the decoder to perform style transfer. As our label-free training results in a style vector space encoding many facets of style, we recast transfers as "targeted restyling" vector operations that adjust specific attributes of the input while preserving others. We demonstrate that training on unlabeled Amazon reviews data results in a model that is competitive on sentiment transfer, even compared to models trained fully on labeled data. Furthermore, applying our novel method to a diverse corpus of unlabeled web text results in a single model capable of transferring along multiple dimensions of style (dialect, emotiveness, formality, politeness, sentiment) despite no additional training and using only a handful of exemplars at inference time.

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