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.- Anthology ID:
- 2021.repl4nlp-1.9
- Volume:
- Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Anna Rogers, Iacer Calixto, Ivan Vulić, Naomi Saphra, Nora Kassner, Oana-Maria Camburu, Trapit Bansal, Vered Shwartz
- Venue:
- RepL4NLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 72–82
- Language:
- URL:
- https://aclanthology.org/2021.repl4nlp-1.9
- DOI:
- 10.18653/v1/2021.repl4nlp-1.9
- Cite (ACL):
- Xiaoyan Li, Sun Sun, and Yunli Wang. 2021. Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations. In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pages 72–82, Online. Association for Computational Linguistics.
- Cite (Informal):
- Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations (Li et al., RepL4NLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.repl4nlp-1.9.pdf