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    <abstract>Text style transfer aims to change an input sentence to an output sentence by changing its text style while preserving the content. Previous efforts on unsupervised text style transfer only use the surface features of words and sentences. As a result, the transferred sentences may either have inaccurate or missing information compared to the inputs. We address this issue by explicitly enriching the inputs via syntactic and semantic structures, from which richer features are then extracted to better capture the original information. Experiments on two text-style-transfer tasks show that our approach improves the content preservation of a strong unsupervised baseline model thereby demonstrating improved transfer performance.</abstract>
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%0 Conference Proceedings
%T Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer
%A Gong, Hongyu
%A Song, Linfeng
%A Bhat, Suma
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 dec
%I Association for Computational Linguistics
%C Dublin, Ireland
%F gong-etal-2020-rich
%X Text style transfer aims to change an input sentence to an output sentence by changing its text style while preserving the content. Previous efforts on unsupervised text style transfer only use the surface features of words and sentences. As a result, the transferred sentences may either have inaccurate or missing information compared to the inputs. We address this issue by explicitly enriching the inputs via syntactic and semantic structures, from which richer features are then extracted to better capture the original information. Experiments on two text-style-transfer tasks show that our approach improves the content preservation of a strong unsupervised baseline model thereby demonstrating improved transfer performance.
%U https://aclanthology.org/2020.inlg-1.17
%P 113-119

Markdown (Informal)

[Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer](https://aclanthology.org/2020.inlg-1.17) (Gong et al., INLG 2020)

• Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer (Gong et al., INLG 2020)

ACL

• Hongyu Gong, Linfeng Song, and Suma Bhat. 2020. Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer. In Proceedings of the 13th International Conference on Natural Language Generation, pages 113–119, Dublin, Ireland. Association for Computational Linguistics.