| Download | - View final version: Text style transfer: leveraging a style classifier on entangled latent representations (PDF, 968 KiB)
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| DOI | Resolve DOI: https://doi.org/10.18653/v1/2021.repl4nlp-1.9 |
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| Author | Search for: Li, Xiaoyan; Search for: Sun, Sun1; Search for: Wang, Yunli1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), Aug. 6th, 2021, Bangkok, Thailand (Online) |
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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. |
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| Publication date | 2021-08-06 |
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| Publisher | Association for Computational Linguistics |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | a6888c23-c792-4543-9ca0-43fa53abc356 |
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| Record created | 2021-09-10 |
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| Record modified | 2021-09-14 |
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