# IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation

arXiv: https://arxiv.org/abs/1901.11333 · HTML: https://mlsanity.com/paper/1901.11333/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 30 of 33)

- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [A Recipe For Arbitrary Text Style Transfer with Large Language Models](https://mlsanity.com/paper/2109.03910/) (2021)
- [XFORMAL: A Benchmark for Multilingual Formality Style Transfer](https://mlsanity.com/paper/2104.04108/) (2021)
- [Civil Rephrases Of Toxic Texts With Self-Supervised Transformers](https://mlsanity.com/paper/2102.05456/) (2021)
- [Inducing Positive Perspectives with Text Reframing](https://mlsanity.com/paper/2204.02952/) (2022)
- [A Review of Human Evaluation for Style Transfer](https://mlsanity.com/paper/2106.04747/) (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)
- [Transductive Learning for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2109.07812/) (2021)
- [Non-Parallel Text Style Transfer with Self-Parallel Supervision](https://mlsanity.com/paper/2204.08123/) (2022)
- [Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance](https://mlsanity.com/paper/2205.02293/) (2022)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs](https://mlsanity.com/paper/2406.04460/) (2024)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [Prefix-Tuning Based Unsupervised Text Style Transfer](https://mlsanity.com/paper/2310.14599/) (2023)
- [Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer](https://mlsanity.com/paper/2110.02950/) (2021)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Audience-Centric Natural Language Generation via Style Infusion](https://mlsanity.com/paper/2301.10283/) (2023)
- [Translating away Translationese without Parallel Data](https://mlsanity.com/paper/2310.18830/) (2023)
- [Text Style Transfer: An Introductory Overview](https://mlsanity.com/paper/2407.14822/) (2024)
- [StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2212.09670/) (2022)
- [SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer](https://mlsanity.com/paper/2406.04578/) (2024)
- [GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer](https://mlsanity.com/paper/2102.00769/) (2021)
- [How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models](https://mlsanity.com/paper/2408.16756/) (2024)
- [Exploiting Social Media Content for Self-Supervised Style Transfer](https://mlsanity.com/paper/2205.08814/) (2022)
- [SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2104.12977/) (2021)
- [Few-Shot Domain Adaptation for Charge Prediction on Unprofessional Descriptions](https://mlsanity.com/paper/2309.17313/) (2023)
- [Positive Text Reframing under Multi-strategy Optimization](https://mlsanity.com/paper/2407.17940/) (2024)
- [Stealthy Backdoor Attacks against LLMs Based on Natural Style Triggers](https://mlsanity.com/paper/2604.21700/) (2026)
- [Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer](https://mlsanity.com/paper/2205.09324/) (2022)

## Abstract (arXiv)

Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content. This task remains challenging due to a lack of supervised parallel data. Existing approaches try to explicitly disentangle content and attribute information, but this is difficult and often results in poor content-preservation and ungrammaticality. In contrast, we propose a simpler approach, Iterative Matching and Translation (IMaT), which: (1) constructs a pseudo-parallel corpus by aligning a subset of semantically similar sentences from the source and the target corpora; (2) applies a standard sequence-to-sequence model to learn the attribute transfer; (3) iteratively improves the learned transfer function by refining imperfections in the alignment. In sentiment modification and formality transfer tasks, our method outperforms complex state-of-the-art systems by a large margin. As an auxiliary contribution, we produce a publicly-available test set with human-generated transfer references.