TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling
By Parker Riley, Noah Constant, Mandy Guo, David C. Uthus, Zarana Parekh. 2021
14 citations recorded by the source corpus.
Subfields: Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Daphne Ippolito
- Jason Wei
- Andy Coenen
- Chris Callison-Burch
- Kory Wallace Mathewson
- Ann Yuan
- Emily Reif
- Kevin Gimpel
Citations in the corpus
- A Recipe for Arbitrary Text Style Transfer with Large Language Models (2021, background): “Even bleeding-edge approaches that tackle the challenging problem of label-free style transfer are limited in that they require at least several exemplar sentences that dictate a given target style (Xu et al., 2020; Riley et al., 2021).”
- Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool (2021, background): “As some recently successful work in style transfer (Riley et al., 2020) already follows a label free approach that might itself be considered few shot in nature, interesting experimental comparisons are likely possible.”