Challenge: Unsupervised Text Style Transfer (UTST) aims to transfer the stylistic properties of a given text without parallel text pairs.
Approach: They propose a SFT-then-PPO paradigm to fine-tune an LLM with parallel data and reward functions for distinguishing stylistic intensity in hierarchical levels.
Outcome: The proposed system can transfer stylistic properties without parallel text pairs even for adjacent levels of intensity.

Similar Papers

PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer (2024.findings-emnlp)

Copied to clipboard

Challenge: Existing methods to transfer text style focus on sentence-level data, limiting performance . current LLMs struggle to generate public speaking texts that align with human preferences .
Approach: They propose a task to transform official texts into public-speaking styles by analyzing real-world data.
Outcome: The proposed task aims to transform public speaking texts into public-speaking styles . the proposed framework analyzes characteristics and identifies problems of stylized texts .
Style-Specific Neurons for Steering LLMs in Text Style Transfer (2024.emnlp-main)

Copied to clipboard

Challenge: Existing LLMs tend to prioritize preserving original meaning over enhancing stylistic differences in TST.
Approach: They propose a novel approach to steering LLMs using style-specific neurons in TST.
Outcome: Empirical results show that the proposed method improves the fluency of the generated text.
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer (2021.naacl-main)

Copied to clipboard

Challenge: Existing methods for text style transfer focus on individual high-level semantic changes but do not offer fine-grained control of sentence structure, emphasis, and content.
Approach: They propose a large-scale text style transfer benchmark with 21 fine-grained stylistic changes across atomic lexical, syntactic, semantic, and thematic transfers.
Outcome: The proposed method allows modeling fine-grained changes as building blocks for more complex, high-level transfers.
Unsupervised Aspect-Level Sentiment Controllable Style Transfer (2020.aacl-main)

Copied to clipboard

Challenge: Unsupervised style transfer has been explored in text.
Approach: They propose a system where aspect-level sentiments can be controlled at the output . they propose to use unsupervised techniques such as ABSA masked-language-modelling .
Outcome: The proposed system is successful in controlling aspect-level sentiments.
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)

Copied to clipboard

Challenge: Existing methods for autoregressive text generation have low controllability and accumulating errors.
Approach: They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy.
Outcome: Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance.
LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer (2021.findings-acl)

Copied to clipboard

Challenge: Recent work on text style transfer proposes single-span editing as an alternative to generating the target text from scratch.
Approach: They propose a coarse-to-fine editor for style transfer that transforms text using Levenshtein edit operations (e.g. insert, replace, delete).
Outcome: The proposed method outperforms existing methods on sentiment and politeness transfer and improves model performance.
Evaluating Text Style Transfer Evaluation: Are There Any Reliable Metrics? (2025.naacl-srw)

Copied to clipboard

Challenge: Text style transfer (TST) is a multidimensional task requiring the assessment of style transfer accuracy, content preservation, and naturalness.
Approach: They propose to use text style transfer metrics to evaluate outputs of text editors . they also investigate the potential of large language models as tools for TST evaluation .
Outcome: The proposed methods provide better insights than existing metrics, the authors show . their meta-evaluation through correlation with hu-man judgments shows they are effective .
Neuron Activation Modulation for Text Style Transfer: Guiding Large Language Models (2025.findings-acl)

Copied to clipboard

Challenge: Text style transfer (TST) aims to flexibly adjust the style of text while preserving its core content.
Approach: They propose a method that aligns activation values of style-related neurons with those of the target style to guide the model in performing the transfer.
Outcome: The proposed method significantly improves style transfer quality while preserving core content.
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling (2021.acl-long)

Copied to clipboard

Challenge: Existing methods for text style transfer require style-labeled training data, but use only labeled data at inference time.
Approach: They propose a method that uses readily-available unlabeled text to train style transfer . they use a style vector to condition a decoder to perform style transfer using unlabelled text .
Outcome: The proposed method is competitive on sentiment transfer, even compared to models trained fully on labeled data.
Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer (N18-1)

Copied to clipboard

Challenge: a lack of training and evaluation datasets, benchmarks and automatic metrics has blocked progress in this field.
Approach: They propose to use a grammarly's Yahoo Answers Formality corpus to create the largest corpus for a particular style . they also propose to apply machine translation metrics to the task .
Outcome: The proposed model can be used to train and evaluate a text in a particular style . the proposed model is based on the existing model and can be applied to other tasks .

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations