Papers with style-labeled training data

1 papers
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling (2021.acl-long)

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Challenge: Existing methods for text style transfer require style-labeled training data, but use only labeled data at inference time.
Approach: They propose a method that uses readily-available unlabeled text to train style transfer . they use a style vector to condition a decoder to perform style transfer using unlabelled text .
Outcome: The proposed method is competitive on sentiment transfer, even compared to models trained fully on labeled data.

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