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    <abstract>Style transfer is the task of rewriting a sentence into a target style while approximately preserving content. While most prior literature assumes access to a large style-labelled corpus, recent work (Riley et al. 2021) has attempted “few-shot” style transfer using only 3-10 sentences at inference for style extraction. In this work we study a relevant low-resource setting: style transfer for languages where no style-labelled corpora are available. We notice that existing few-shot methods perform this task poorly, often copying inputs verbatim. We push the state-of-the-art for few-shot style transfer with a new method modeling the stylistic difference between paraphrases. When compared to prior work, our model achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages. Moreover, our method is better at controlling the style transfer magnitude using an input scalar knob. We report promising qualitative results for several attribute transfer tasks (sentiment transfer, simplification, gender neutralization, text anonymization) all without retraining the model. Finally, we find model evaluation to be difficult due to the lack of datasets and metrics for many languages. To facilitate future research we crowdsource formality annotations for 4000 sentence pairs in four Indic languages, and use this data to design our automatic evaluations.</abstract>
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%0 Conference Proceedings
%T Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings
%A Krishna, Kalpesh
%A Nathani, Deepak
%A Garcia, Xavier
%A Samanta, Bidisha
%A Talukdar, Partha
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F krishna-etal-2022-shot
%X Style transfer is the task of rewriting a sentence into a target style while approximately preserving content. While most prior literature assumes access to a large style-labelled corpus, recent work (Riley et al. 2021) has attempted “few-shot” style transfer using only 3-10 sentences at inference for style extraction. In this work we study a relevant low-resource setting: style transfer for languages where no style-labelled corpora are available. We notice that existing few-shot methods perform this task poorly, often copying inputs verbatim. We push the state-of-the-art for few-shot style transfer with a new method modeling the stylistic difference between paraphrases. When compared to prior work, our model achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages. Moreover, our method is better at controlling the style transfer magnitude using an input scalar knob. We report promising qualitative results for several attribute transfer tasks (sentiment transfer, simplification, gender neutralization, text anonymization) all without retraining the model. Finally, we find model evaluation to be difficult due to the lack of datasets and metrics for many languages. To facilitate future research we crowdsource formality annotations for 4000 sentence pairs in four Indic languages, and use this data to design our automatic evaluations.
%R 10.18653/v1/2022.acl-long.514
%U https://aclanthology.org/2022.acl-long.514
%U https://doi.org/10.18653/v1/2022.acl-long.514
%P 7439-7468

Markdown (Informal)

[Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings](https://aclanthology.org/2022.acl-long.514) (Krishna et al., ACL 2022)

• Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings (Krishna et al., ACL 2022)

ACL

• Kalpesh Krishna, Deepak Nathani, Xavier Garcia, Bidisha Samanta, and Partha Talukdar. 2022. Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7439–7468, Dublin, Ireland. Association for Computational Linguistics.