mlsanityData to 14 Oct 2026

Style Transfer Through Back-Translation

arXiv 1804.09000 ↗First posted 24 Apr 2018cs.CLcited by 124

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Abstract (arXiv)

Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context. This paper introduces a new method for automatic style transfer. We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties. Then adversarial generation techniques are used to make the output match the desired style. We evaluate this technique on three different style transformations: sentiment, gender and political slant. Compared to two state-of-the-art style transfer modeling techniques we show improvements both in automatic evaluation of style transfer and in manual evaluation of meaning preservation and fluency.

Key facts. Cited by 124 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Apr 2018.

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Citing papers per year: 2021: 37; 2022: 32; 2023: 23; 2024: 18; 2025: 6; 2026: 8.

124 papers.

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  1. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  2. 2022Challenges and Strategies in Cross-Cultural NLPcited by 218
  3. 2022Generating Training Data with Language Models: Towards Zero-Shot Language Understandingcited by 199
  4. 2023AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotatorscited by 172
  5. 2021Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfercited by 158
  6. 2021A Recipe For Arbitrary Text Style Transfer with Large Language Modelscited by 107
  7. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  8. 2021Controlled Text Generation as Continuous Optimization with Multiple Constraintscited by 70
  9. 2022Gradient-Based Constrained Sampling from Language Modelscited by 64
  10. 2022LENS: A Learnable Evaluation Metric for Text Simplificationcited by 54
  11. 2021XFORMAL: A Benchmark for Multilingual Formality Style Transfercited by 52
  12. 2021Text Detoxification using Large Pre-trained Neural Modelscited by 52
38 more
  1. 2022Perturbation Augmentation for Fairer NLPcited by 52
  2. 2022Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Modelscited by 49
  3. 2022Automatic Generation of Socratic Subquestions for Teaching Math Word Problemscited by 43
  4. 2021LEWIS: Levenshtein Editing for Unsupervised Text Style Transfercited by 40
  5. 2023Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Tablecited by 40
  6. 2021Meta-Learning Dynamics Forecasting Using Task Inferencecited by 38
  7. 2021A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representationscited by 37
  8. 2022Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learningcited by 36
  9. 2021StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfercited by 34
  10. 2022Topic Discovery via Latent Space Clustering of Pretrained Language Model Representationscited by 34
  11. 2021Civil Rephrases Of Toxic Texts With Self-Supervised Transformerscited by 32
  12. 2022Inducing Positive Perspectives with Text Reframingcited by 31
  13. 2023Learning Interpretable Style Embeddings via Prompting LLMscited by 30
  14. 2021Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfercited by 28
  15. 2022Text Style Transfer for Bias Mitigation using Masked Language Modelingcited by 28
  16. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  17. 2021Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decodingcited by 23
  18. 2021Neural Data-to-Text Generation with LM-based Text Augmentationcited by 22
  19. 2021Few-shot Controllable Style Transfer for Low-Resource Multilingual Settingscited by 21
  20. 2021Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalizationcited by 20
  21. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  22. 2021Semi-Supervised Graph-to-Graph Translationcited by 18
  23. 2021A Review of Text Style Transfer using Deep Learningcited by 18
  24. 2021Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generationcited by 17
  25. 2024Can AI Be as Creative as Humans?cited by 17
  26. 2023Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Textscited by 16
  27. 2023Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategycited by 15
  28. 2023A Call for Standardization and Validation of Text Style Transfer Evaluationcited by 14
  29. 2023STEER: Unified Style Transfer with Expert Reinforcementcited by 14
  30. 2021A Survey of Natural Language Generationcited by 13
  31. 2024Are Large Language Models Actually Good at Text Style Transfer?cited by 13
  32. 2021Countering Online Hate Speech: An NLP Perspectivecited by 12
  33. 2021Transductive Learning for Unsupervised Text Style Transfercited by 12
  34. 2022Semi-Supervised Formality Style Transfer with Consistency Trainingcited by 12
  35. 2022Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfercited by 12
  36. 2023She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Modelscited by 12
  37. 2021Time-Aware Ancient Chinese Text Translation and Inferencecited by 11
  38. 2024Text Detoxification as Style Transfer in English and Hindicited by 11

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