mlsanityData to 7 Oct 2026

Parallel Data Augmentation for Formality Style Transfer

arXiv 2005.07522 ↗First posted 14 May 2020cs.CLcited by 39

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Abstract (arXiv)

The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation methods for this task to obtain useful sentence pairs with easily accessible models and systems. Experiments demonstrate that our augmented parallel data largely helps improve formality style transfer when it is used to pre-train the model, leading to the state-of-the-art results in the GYAFC benchmark dataset.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 May 2020.

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Citing papers per year: 2021: 11; 2022: 10; 2023: 9; 2024: 5; 2025: 3; 2026: 1.

39 papers.

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  1. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  2. 2023RewriteLM: An Instruction-Tuned Large Language Model for Text Rewritingcited by 67
  3. 2021XFORMAL: A Benchmark for Multilingual Formality Style Transfercited by 51
  4. 2021A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representationscited by 37
  5. 2022Inducing Positive Perspectives with Text Reframingcited by 31
  6. 2021Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfercited by 28
  7. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  8. 2023Don't lose the message while paraphrasing: A study on content preserving style transfercited by 25
  9. 2021Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfercited by 23
  10. 2022Kallima: A Clean-label Framework for Textual Backdoor Attackscited by 22
  11. 2023Prompt-Based Editing for Text Style Transfercited by 16
  12. 2021Text AutoAugment: Learning Compositional Augmentation Policy for Text Classificationcited by 15
27 more
  1. 2021Improving Formality Style Transfer with Context-Aware Rule Injectioncited by 13
  2. 2023A Call for Standardization and Validation of Text Style Transfer Evaluationcited by 13
  3. 2022Semi-Supervised Formality Style Transfer with Consistency Trainingcited by 12
  4. 2023Multidimensional Evaluation for Text Style Transfer Using ChatGPTcited by 12
  5. 2024Distilling Text Style Transfer With Self-Explanation From LLMscited by 12
  6. 2025LLM-Friendly Knowledge Representation for Customer Supportcited by 11
  7. 2024DetoxLLM: A Framework for Detoxification with Explanationscited by 10
  8. 2022Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfercited by 8
  9. 2023Towards an On-device Agent for Text Rewritingcited by 7
  10. 2024Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactionscited by 7
  11. 2022Extensible Prompts for Language Models on Zero-shot Language Style Customizationcited by 6
  12. 2021From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Surveycited by 5
  13. 2024Proofread: Fixes All Errors with One Tapcited by 4
  14. 2024Style Transfer with Multi-iteration Preference Optimizationcited by 4
  15. 2025LLM in the Loop: Creating the ParaDeHate Dataset for Hate Speech Detoxificationcited by 4
  16. 2022Robust Self-Augmentation for Named Entity Recognition with Meta Reweightingcited by 3
  17. 2022Exploring Cross-lingual Textual Style Transfer with Large Multilingual Language Modelscited by 3
  18. 2023Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfercited by 2
  19. 2025Dr Genre: Reinforcement Learning from Decoupled LLM Feedback for Generic Text Rewritingcited by 2
  20. 2021Contextualizing Variation in Text Style Transfer Datasetscited by 1
  21. 2021Empirical Evaluation of Supervision Signals for Style Transfer Modelscited by 0
  22. 2022Doctor Recommendation in Online Health Forums via Expertise Learningcited by 0
  23. 2022Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfercited by 0
  24. 2022SimpleStyle: An Adaptable Style Transfer Approachcited by 0
  25. 2023Investigating Masking-based Data Generation in Language Modelscited by 0
  26. 2023Text Fact Transfercited by 0
  27. 2026SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extractioncited by 0