# Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/1805.05181 · HTML: https://mlsanity.com/paper/1805.05181/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2018.

## Built on by (most cited first, 30 of 55)

- [Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer](https://mlsanity.com/paper/2110.07139/) (2021)
- [A Recipe For Arbitrary Text Style Transfer with Large Language Models](https://mlsanity.com/paper/2109.03910/) (2021)
- [Controlled Text Generation with Natural Language Instructions](https://mlsanity.com/paper/2304.14293/) (2023)
- [XFORMAL: A Benchmark for Multilingual Formality Style Transfer](https://mlsanity.com/paper/2104.04108/) (2021)
- [Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models](https://mlsanity.com/paper/2205.11503/) (2022)
- [Civil Rephrases Of Toxic Texts With Self-Supervised Transformers](https://mlsanity.com/paper/2102.05456/) (2021)
- [Parallel Refinements for Lexically Constrained Text Generation with BART](https://mlsanity.com/paper/2109.12487/) (2021)
- [Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization](https://mlsanity.com/paper/2108.00449/) (2021)
- [StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements](https://mlsanity.com/paper/2408.15666/) (2024)
- [Text Revision by On-the-Fly Representation Optimization](https://mlsanity.com/paper/2204.07359/) (2022)
- [Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization](https://mlsanity.com/paper/2206.04007/) (2022)
- [Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation](https://mlsanity.com/paper/2301.04907/) (2023)
- [A Call for Standardization and Validation of Text Style Transfer Evaluation](https://mlsanity.com/paper/2306.00539/) (2023)
- [STEER: Unified Style Transfer with Expert Reinforcement](https://mlsanity.com/paper/2311.07167/) (2023)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [Transductive Learning for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2109.07812/) (2021)
- [Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer](https://mlsanity.com/paper/2204.07549/) (2022)
- [Generic resources are what you need: Style transfer tasks without task-specific parallel training data](https://mlsanity.com/paper/2109.04543/) (2021)
- [Diversify Question Generation with Retrieval-Augmented Style Transfer](https://mlsanity.com/paper/2310.14503/) (2023)
- [Political Depolarization of News Articles Using Attribute-aware Word Embeddings](https://mlsanity.com/paper/2101.01391/) (2021)
- [On The Robustness of Offensive Language Classifiers](https://mlsanity.com/paper/2203.11331/) (2022)
- [StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing](https://mlsanity.com/paper/2208.13423/) (2022)
- [Efficient Reinforcement Learning for Unsupervised Controlled Text Generation](https://mlsanity.com/paper/2204.07696/) (2022)
- [Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions](https://mlsanity.com/paper/2402.13647/) (2024)
- [LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback](https://mlsanity.com/paper/2406.03363/) (2024)
- [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 Solving a Problem Boldly to Cutting the Gordian Knot: Idiomatic Text Generation](https://mlsanity.com/paper/2104.06541/) (2021)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Translating away Translationese without Parallel Data](https://mlsanity.com/paper/2310.18830/) (2023)

## Abstract (arXiv)

The goal of sentiment-to-sentiment "translation" is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neutralization module and an emotionalization module. We evaluate our approach on two review datasets, Yelp and Amazon. Experimental results show that our approach significantly outperforms the state-of-the-art systems. Especially, the proposed method substantially improves the content preservation performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to 14.06 on the two datasets, respectively.