# Reinforced Rewards Framework for Text Style Transfer

arXiv: https://arxiv.org/abs/2005.05256 · HTML: https://mlsanity.com/paper/2005.05256/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 12 of 12)

- [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)
- [A Call for Standardization and Validation of Text Style Transfer Evaluation](https://mlsanity.com/paper/2306.00539/) (2023)
- [Multidimensional Evaluation for Text Style Transfer Using ChatGPT](https://mlsanity.com/paper/2304.13462/) (2023)
- [Generic resources are what you need: Style transfer tasks without task-specific parallel training data](https://mlsanity.com/paper/2109.04543/) (2021)
- [Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer](https://mlsanity.com/paper/2203.08552/) (2022)
- [LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback](https://mlsanity.com/paper/2406.03363/) (2024)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization](https://mlsanity.com/paper/2311.00807/) (2023)
- [Entailment Relation Aware Paraphrase Generation](https://mlsanity.com/paper/2203.10483/) (2022)
- [Positive Text Reframing under Multi-strategy Optimization](https://mlsanity.com/paper/2407.17940/) (2024)
- [Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer](https://mlsanity.com/paper/2205.09324/) (2022)

## Abstract (arXiv)

Style transfer deals with the algorithms to transfer the stylistic properties of a piece of text into that of another while ensuring that the core content is preserved. There has been a lot of interest in the field of text style transfer due to its wide application to tailored text generation. Existing works evaluate the style transfer models based on content preservation and transfer strength. In this work, we propose a reinforcement learning based framework that directly rewards the framework on these target metrics yielding a better transfer of the target style. We show the improved performance of our proposed framework based on automatic and human evaluation on three independent tasks: wherein we transfer the style of text from formal to informal, high excitement to low excitement, modern English to Shakespearean English, and vice-versa in all the three cases. Improved performance of the proposed framework over existing state-of-the-art frameworks indicates the viability of the approach.