Semi-supervised Text Style Transfer: Cross Projection in Latent Space

Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, Rui Yan

Abstract
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data. In this paper, we first propose a semi-supervised text style transfer model that combines the small-scale parallel data with the large-scale nonparallel data. With these two types of training data, we introduce a projection function between the latent space of different styles and design two constraints to train it. We also introduce two other simple but effective semi-supervised methods to compare with. To evaluate the performance of the proposed methods, we build and release a novel style transfer dataset that alters sentences between the style of ancient Chinese poem and the modern Chinese.
Anthology ID:
D19-1499
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4937–4946
Language:
URL:
https://aclanthology.org/D19-1499
DOI:
10.18653/v1/D19-1499
Bibkey:
Cite (ACL):
Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, and Rui Yan. 2019. Semi-supervised Text Style Transfer: Cross Projection in Latent Space. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4937–4946, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Semi-supervised Text Style Transfer: Cross Projection in Latent Space (Shang et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/ingest-2024-clasp/D19-1499.pdf
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