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Few-shot Controllable Style Transfer for Low-Resource Languages

Kalpesh Krishna
Deepak Nathani
Xavier Garcia
Bidisha Samanta
Partha Pratim Talukdar
Few-shot Controllable Style Transfer for Low-Resource Languages (2022)
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Abstract

Few-shot style transfer is the task of rewriting an input sentence using the stylistic properties extracted from a few (3-10) exemplar sentences, while approximately preserving the input content. This is especially useful in low resource settings where no large style-transfer datasets are available. We push the state-of-the-art for few-shot style transfer with a new method that models the stylistic difference between paraphrases. When compared to prior work our method achieves better performance in , output diversity and style transfer magnitude control for five Indian Languages.