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A Review of Text Style Transfer using Deep Learning

arXiv 2109.15144 ↗First posted 30 Sep 2021cs.CL cs.AIcited by 18

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Abstract (arXiv)

Style is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves, however, they adjust their speaking and writing style to a social context, an audience, an interlocutor or the formality of an occasion. Text style transfer is defined as a task of adapting and/or changing the stylistic manner in which a sentence is written, while preserving the meaning of the original sentence. A systematic review of text style transfer methodologies using deep learning is presented in this paper. We point out the technological advances in deep neural networks that have been the driving force behind current successes in the fields of natural language understanding and generation. The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. The discussion highlights the commonalities and differences between proposed solutions as well as challenges and opportunities that are expected to direct and foster further research in the field.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 82 of its 170 references that are in the map. First posted on arXiv 30 Sep 2021.

Builds on

82 of 170 references.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 5; 2024: 5; 2025: 2; 2026: 4.

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  1. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  2. 2023A Call for Standardization and Validation of Text Style Transfer Evaluationcited by 14
  3. 2023Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-artcited by 14
  4. 2024Constraint Back-translation Improves Complex Instruction Following of Large Language Modelscited by 11
  5. 2024CAT-LLM: Style-enhanced Large Language Models with Text Style Definition for Chinese Article-style Transfercited by 10
  6. 2023Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware Denoisingcited by 7
  7. 2024AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behaviorcited by 6
  8. 2021From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Surveycited by 5
  9. 2024Text Style Transfer: An Introductory Overviewcited by 5
  10. 2023Feature-aware conditional GAN for category text generationcited by 2
  11. 2026TSM-Bench: Detecting LLM-Generated Text in Real-World Wikipedia Editing Practicescited by 2
  12. 2023GlyphDiffusion: Text Generation as Image Generationcited by 1
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  2. 2025Test-Time-Matching: Decouple Personality, Memory, and Linguistic Style in LLM-based Role-Playing Language Agentcited by 1
  3. 2024A Survey of Text Style Transfer: Applications and Ethical Implicationscited by 0
  4. 2026Are LLMs More Skeptical of Entertainment News?cited by 0
  5. 2026AEGIS: Awareness-Enhanced Guidance for Iterative Safeguardcited by 0
  6. 2026When Does Span-Guided Detoxification Help? Human Preferences and Evaluator Diagnostics in a Controlled Comparisoncited by 0