# Domain Adaptive Text Style Transfer

arXiv: https://arxiv.org/abs/1908.09395 · HTML: https://mlsanity.com/paper/1908.09395/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2019.

## Built on by (most cited first, 25 of 25)

- [XFORMAL: A Benchmark for Multilingual Formality Style Transfer](https://mlsanity.com/paper/2104.04108/) (2021)
- [Inducing Positive Perspectives with Text Reframing](https://mlsanity.com/paper/2204.02952/) (2022)
- [Learning Interpretable Style Embeddings via Prompting LLMs](https://mlsanity.com/paper/2305.12696/) (2023)
- [Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer](https://mlsanity.com/paper/2110.10668/) (2021)
- [A Review of Human Evaluation for Style Transfer](https://mlsanity.com/paper/2106.04747/) (2021)
- [A Call for Standardization and Validation of Text Style Transfer Evaluation](https://mlsanity.com/paper/2306.00539/) (2023)
- [Counterfactual Generation with Identifiability Guarantees](https://mlsanity.com/paper/2402.15309/) (2024)
- [Geodesic Optimization for Predictive Shift Adaptation on EEG data](https://mlsanity.com/paper/2407.03878/) (2024)
- [Domain Regeneration: How well do LLMs match syntactic properties of text domains?](https://mlsanity.com/paper/2505.07784/) (2025)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Preventing Author Profiling through Zero-Shot Multilingual Back-Translation](https://mlsanity.com/paper/2109.09133/) (2021)
- [Low Resource Style Transfer via Domain Adaptive Meta Learning](https://mlsanity.com/paper/2205.12475/) (2022)
- [StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2212.09670/) (2022)
- [Syntax Matters! Syntax-Controlled in Text Style Transfer](https://mlsanity.com/paper/2108.05869/) (2021)
- [Are Current Task-oriented Dialogue Systems Able to Satisfy Impolite Users?](https://mlsanity.com/paper/2210.12942/) (2022)
- [Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer](https://mlsanity.com/paper/2305.05945/) (2023)
- [Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer](https://mlsanity.com/paper/2305.15582/) (2023)
- [Content-Style Identification via Differential Independence](https://mlsanity.com/paper/2605.17827/) (2026)
- [Contextualizing Variation in Text Style Transfer Datasets](https://mlsanity.com/paper/2108.07871/) (2021)
- [Extending the Vocabulary of Fictional Languages using Neural Networks](https://mlsanity.com/paper/2201.07288/) (2022)
- [Emotional RAG LLMs: Reading Comprehension for the Open Internet](https://mlsanity.com/paper/2408.11189/) (2024)
- [SynGen-Vision: Synthetic Data Generation for training industrial vision models](https://mlsanity.com/paper/2509.04894/) (2025)
- [IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models](https://mlsanity.com/paper/2404.13504/) (2024)
- [A Survey of Text Style Transfer: Applications and Ethical Implications](https://mlsanity.com/paper/2407.16737/) (2024)
- [Neurobiber: Fast and Interpretable Stylistic Feature Extraction](https://mlsanity.com/paper/2502.18590/) (2025)

## Abstract (arXiv)

Text style transfer without parallel data has achieved some practical success. However, in the scenario where less data is available, these methods may yield poor performance. In this paper, we examine domain adaptation for text style transfer to leverage massively available data from other domains. These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training. To address this challenge, we propose simple yet effective domain adaptive text style transfer models, enabling domain-adaptive information exchange. The proposed models presumably learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information; and (iii) adaptively transfer the styles in a domain-aware manner. We evaluate the proposed models on two style transfer tasks (sentiment and formality) over multiple target domains where only limited non-parallel data is available. Extensive experiments demonstrate the effectiveness of the proposed model compared to the baselines.