# Contextual Text Style Transfer

arXiv: https://arxiv.org/abs/2005.00136 · HTML: https://mlsanity.com/paper/2005.00136/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 17 of 17)

- [Controlled Text Generation as Continuous Optimization with Multiple Constraints](https://mlsanity.com/paper/2108.01850/) (2021)
- [ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer](https://mlsanity.com/paper/2306.07799/) (2023)
- [Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts](https://mlsanity.com/paper/2212.10543/) (2022)
- [Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer](https://mlsanity.com/paper/2110.10668/) (2021)
- [APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations](https://mlsanity.com/paper/2209.08207/) (2022)
- [A Review of Text Style Transfer using Deep Learning](https://mlsanity.com/paper/2109.15144/) (2021)
- [A Call for Standardization and Validation of Text Style Transfer Evaluation](https://mlsanity.com/paper/2306.00539/) (2023)
- [Transferable Persona-Grounded Dialogues via Grounded Minimal Edits](https://mlsanity.com/paper/2109.07713/) (2021)
- [T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation](https://mlsanity.com/paper/2212.01667/) (2022)
- [Don't Take This Out of Context! On the Need for Contextual Models and Evaluations for Stylistic Rewriting](https://mlsanity.com/paper/2305.14755/) (2023)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Conversation Style Transfer using Few-Shot Learning](https://mlsanity.com/paper/2302.08362/) (2023)
- [SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer](https://mlsanity.com/paper/2406.04578/) (2024)
- [Syntax Customized Video Captioning by Imitating Exemplar Sentences](https://mlsanity.com/paper/2112.01062/) (2021)
- [ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses](https://mlsanity.com/paper/2403.09131/) (2024)
- [A Survey of Text Style Transfer: Applications and Ethical Implications](https://mlsanity.com/paper/2407.16737/) (2024)
- [ConvoDrift: A Multi-Turn Conversational Dataset for Modeling Stylistic Tone Evolution](https://mlsanity.com/paper/2610.02873/) (2026)

## Abstract (arXiv)

We introduce a new task, Contextual Text Style Transfer - translating a sentence into a desired style with its surrounding context taken into account. This brings two key challenges to existing style transfer approaches: (i) how to preserve the semantic meaning of target sentence and its consistency with surrounding context during transfer; (ii) how to train a robust model with limited labeled data accompanied with context. To realize high-quality style transfer with natural context preservation, we propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context. A classifier is further trained to ensure contextual consistency of the generated sentence. To compensate for the lack of parallel data, additional self-reconstruction and back-translation losses are introduced to leverage non-parallel data in a semi-supervised fashion. Two new benchmarks, Enron-Context and Reddit-Context, are introduced for formality and offensiveness style transfer. Experimental results on these datasets demonstrate the effectiveness of the proposed CAST model over state-of-the-art methods across style accuracy, content preservation and contextual consistency metrics.