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        <title>Parallel Data Augmentation for Formality Style Transfer</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation methods for this task to obtain useful sentence pairs with easily accessible models and systems. Experiments demonstrate that our augmented parallel data largely helps improve formality style transfer when it is used to pre-train the model, leading to the state-of-the-art results in the GYAFC benchmark dataset.</abstract>
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%0 Conference Proceedings
%T Parallel Data Augmentation for Formality Style Transfer
%A Zhang, Yi
%A Ge, Tao
%A Sun, Xu
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-parallel
%X The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation methods for this task to obtain useful sentence pairs with easily accessible models and systems. Experiments demonstrate that our augmented parallel data largely helps improve formality style transfer when it is used to pre-train the model, leading to the state-of-the-art results in the GYAFC benchmark dataset.
%R 10.18653/v1/2020.acl-main.294
%U https://aclanthology.org/2020.acl-main.294
%U https://doi.org/10.18653/v1/2020.acl-main.294
%P 3221-3228

Markdown (Informal)

[Parallel Data Augmentation for Formality Style Transfer](https://aclanthology.org/2020.acl-main.294) (Zhang et al., ACL 2020)

• Parallel Data Augmentation for Formality Style Transfer (Zhang et al., ACL 2020)

ACL

• Yi Zhang, Tao Ge, and Xu Sun. 2020. Parallel Data Augmentation for Formality Style Transfer. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3221–3228, Online. Association for Computational Linguistics.