DGST: a Dual-Generator Network for Text Style Transfer
Publication date
2020-11-15
Authors
Li, Xiao
Chen, Guanyi
Lin, Chenghua
Li, Ruizhe
Editors
Webber, Bonnie
Cohn, Trevor
He, Yulan
Liu, Yang
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Supervisors
Document Type
Part of book
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Abstract
We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training. Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several strong baselines with more complicated architecture designs.
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Li, X, Chen, G, Lin, C & Li, R 2020, DGST: a Dual-Generator Network for Text Style Transfer. in B Webber, T Cohn, Y He & Y Liu (eds), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pp. 7131-7136, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 15/11/20. https://doi.org/10.18653/v1/2020.emnlp-main.578, conference