Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer

Hongyu Gong, Linfeng Song, Suma Bhat


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.
Anthology ID:
2020.inlg-1.17
Volume:
Proceedings of the 13th International Conference on Natural Language Generation
Month:
December
Year:
2020
Address:
Dublin, Ireland
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
113–119
Language:
URL:
https://www.aclweb.org/anthology/2020.inlg-1.17
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.inlg-1.17.pdf
Supplementary attachment:
 2020.inlg-1.17.Supplementary_Attachment.pdf