@inproceedings{li-etal-2021-text,
title = "Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations",
author = "Li, Xiaoyan and
Sun, Sun and
Wang, Yunli",
editor = "Rogers, Anna and
Calixto, Iacer and
Vuli{\'c}, Ivan and
Saphra, Naomi and
Kassner, Nora and
Camburu, Oana-Maria and
Bansal, Trapit and
Shwartz, Vered",
booktitle = "Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.repl4nlp-1.9",
doi = "10.18653/v1/2021.repl4nlp-1.9",
pages = "72--82",
abstract = "Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous works adopt disentangled latent representation learning to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches.",
}
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<abstract>Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous works adopt disentangled latent representation learning to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches.</abstract>
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%0 Conference Proceedings
%T Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations
%A Li, Xiaoyan
%A Sun, Sun
%A Wang, Yunli
%Y Rogers, Anna
%Y Calixto, Iacer
%Y Vulić, Ivan
%Y Saphra, Naomi
%Y Kassner, Nora
%Y Camburu, Oana-Maria
%Y Bansal, Trapit
%Y Shwartz, Vered
%S Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F li-etal-2021-text
%X Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous works adopt disentangled latent representation learning to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches.
%R 10.18653/v1/2021.repl4nlp-1.9
%U https://aclanthology.org/2021.repl4nlp-1.9
%U https://doi.org/10.18653/v1/2021.repl4nlp-1.9
%P 72-82
Markdown (Informal)
[Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations](https://aclanthology.org/2021.repl4nlp-1.9) (Li et al., RepL4NLP 2021)
ACL