# Semi-supervised Text Style Transfer: Cross Projection in Latent Space

arXiv: https://arxiv.org/abs/1909.11493 · HTML: https://mlsanity.com/paper/1909.11493/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 21 of 21)

- [XFORMAL: A Benchmark for Multilingual Formality Style Transfer](https://mlsanity.com/paper/2104.04108/) (2021)
- [MReD: A Meta-Review Dataset for Structure-Controllable Text Generation](https://mlsanity.com/paper/2110.07474/) (2021)
- [Inducing Positive Perspectives with Text Reframing](https://mlsanity.com/paper/2204.02952/) (2022)
- [Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer](https://mlsanity.com/paper/2105.06947/) (2021)
- [Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer](https://mlsanity.com/paper/2110.10668/) (2021)
- [Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings](https://mlsanity.com/paper/2110.07385/) (2021)
- [Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy](https://mlsanity.com/paper/2305.10195/) (2023)
- [A Call for Standardization and Validation of Text Style Transfer Evaluation](https://mlsanity.com/paper/2306.00539/) (2023)
- [Inference Time Style Control for Summarization](https://mlsanity.com/paper/2104.01724/) (2021)
- [Counterfactual Generation with Identifiability Guarantees](https://mlsanity.com/paper/2402.15309/) (2024)
- [Semi-Supervised Formality Style Transfer with Consistency Training](https://mlsanity.com/paper/2203.13620/) (2022)
- [Distilling Text Style Transfer With Self-Explanation From LLMs](https://mlsanity.com/paper/2403.01106/) (2024)
- [Time-Aware Ancient Chinese Text Translation and Inference](https://mlsanity.com/paper/2107.03179/) (2021)
- [Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions](https://mlsanity.com/paper/2402.13647/) (2024)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2212.09670/) (2022)
- [Style Transfer with Multi-iteration Preference Optimization](https://mlsanity.com/paper/2406.11581/) (2024)
- [Evaluating Style-Personalized Text Generation: Challenges and Directions](https://mlsanity.com/paper/2508.06374/) (2025)
- [Generative Sentiment Transfer via Adaptive Masking](https://mlsanity.com/paper/2302.12045/) (2023)
- [Unsupervised Text Style Transfer with Deep Generative Models](https://mlsanity.com/paper/2308.16584/) (2023)
- [A Survey of Text Style Transfer: Applications and Ethical Implications](https://mlsanity.com/paper/2407.16737/) (2024)

## Abstract (arXiv)

Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data. In this paper, we first propose a semi-supervised text style transfer model that combines the small-scale parallel data with the large-scale nonparallel data. With these two types of training data, we introduce a projection function between the latent space of different styles and design two constraints to train it. We also introduce two other simple but effective semi-supervised methods to compare with. To evaluate the performance of the proposed methods, we build and release a novel style transfer dataset that alters sentences between the style of ancient Chinese poem and the modern Chinese.