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        <title>Contextualizing Variation in Text Style Transfer Datasets</title>
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    <abstract>Text style transfer involves rewriting the content of a source sentence in a target style. Despite there being a number of style tasks with available data, there has been limited systematic discussion of how text style datasets relate to each other. This understanding, however, is likely to have implications for selecting multiple data sources for model training. While it is prudent to consider inherent stylistic properties when determining these relationships, we also must consider how a style is realized in a particular dataset. In this paper, we conduct several empirical analyses of existing text style datasets. Based on our results, we propose a categorization of stylistic and dataset properties to consider when utilizing or comparing text style datasets.</abstract>
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%0 Conference Proceedings
%T Contextualizing Variation in Text Style Transfer Datasets
%A Schoch, Stephanie
%A Du, Wanyu
%A Ji, Yangfeng
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F schoch-etal-2021-contextualizing
%X Text style transfer involves rewriting the content of a source sentence in a target style. Despite there being a number of style tasks with available data, there has been limited systematic discussion of how text style datasets relate to each other. This understanding, however, is likely to have implications for selecting multiple data sources for model training. While it is prudent to consider inherent stylistic properties when determining these relationships, we also must consider how a style is realized in a particular dataset. In this paper, we conduct several empirical analyses of existing text style datasets. Based on our results, we propose a categorization of stylistic and dataset properties to consider when utilizing or comparing text style datasets.
%R 10.18653/v1/2021.inlg-1.22
%U https://aclanthology.org/2021.inlg-1.22/
%U https://doi.org/10.18653/v1/2021.inlg-1.22
%P 226-239

Markdown (Informal)

[Contextualizing Variation in Text Style Transfer Datasets](https://aclanthology.org/2021.inlg-1.22/) (Schoch et al., INLG 2021)

• Contextualizing Variation in Text Style Transfer Datasets (Schoch et al., INLG 2021)

ACL

• Stephanie Schoch, Wanyu Du, and Yangfeng Ji. 2021. Contextualizing Variation in Text Style Transfer Datasets. In Proceedings of the 14th International Conference on Natural Language Generation, pages 226–239, Aberdeen, Scotland, UK. Association for Computational Linguistics.