Papers with Text style transfer
Story-level Text Style Transfer: A Proposal (2020.acl-srw)
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| Challenge: | Text style transfer is a task that aims to change the style of input text to the target style while preserving the content to some extent. |
| Approach: | They propose to use story-level text style transfer to generate stories that preserve plot . they propose to employ BERT-based method, Story Realization method, and Graph-based methods . |
| Outcome: | The proposed method preserves the plot of the input story while exhibiting a strong target style. |
Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization (2021.acl-long)
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| Challenge: | Text style transfer aims to alter the style of a sentence while preserving its content. |
| Approach: | They propose to remove style information at token level and fuse it to style representations using conditional layer normalization. |
| Outcome: | The proposed model outperforms the state-of-the-art models in terms of content preservation and fluency. |
A large-scale computational study of content preservation measures for text style transfer and paraphrase generation (2022.acl-srw)
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| Challenge: | Text style transfer and paraphrases generation are growing areas of NLP . many researchers still use BLEU-like measures to evaluate content preservation . |
| Approach: | They compare 57 different measures based on different principles on 19 annotated datasets . they find that measures relying on cross-encoder models outperform alternative approaches . |
| Outcome: | The proposed methods outperform traditional methods on 19 datasets. |
Evaluating Text Style Transfer Evaluation: Are There Any Reliable Metrics? (2025.naacl-srw)
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| 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 . |
Reusing Transferable Weight Increments for Low-resource Style Generation (2024.emnlp-main)
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| Challenge: | Text style transfer (TST) is crucial in natural language processing, aiming to endow text with a new style without altering its meaning. |
| Approach: | They propose a framework to use style features in weight increments to transfer low-resource styles effectively. |
| Outcome: | The proposed framework achieves remarkable performance across different backbones, achieving particularly effective results in low-resource scenarios. |
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer (2021.naacl-main)
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Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency
| 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. |
Text Style Transfer via Optimal Transport (2022.naacl-main)
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| Challenge: | Text style transfer (TST) is a task that aims to change the style of a text from source to target while preserving its content. |
| Approach: | They propose a method to incorporate syntactic and semantic information into similarity computation between the source and the converted text. |
| Outcome: | The proposed method is superior in both supervised and unsupervised settings. |
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer (2022.findings-naacl)
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| Challenge: | Text style transfer is an important task in controllable language generation due to the scarcity of large-scale parallel data. |
| Approach: | They propose a semi-supervised framework for text style transfer that bootstraps with supervision guided by automatically constructed pseudo-parallel pairs and improves the sequence-to-sequence policy gradient via reinforcement rewards. |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiple datasets and produces effective generation with as minimal as 10% of training data. |
Low Resource Style Transfer via Domain Adaptive Meta Learning (2022.naacl-main)
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| Challenge: | Existing unsupervised text style transfer methods suffer from performance degradation when fine-tuning the model in new domains. |
| Approach: | They propose a domain adaptive meta-learning approach with an adversarial style training approach for better content preservation and style transfer. |
| Outcome: | The proposed approach generalizes well on unseen low-resource domains against ten strong baselines. |
Balancing the Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer (2023.findings-acl)
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| Challenge: | Text style transfer requires a high-quality paired dataset and quality training data. |
| Approach: | They propose to use a pseudo-parallel dataset to adjust the style distribution in training data to balance the style transfer model. |
| Outcome: | The proposed model produces more effective control effects over multiple styles than an imbalanced or skewed one. |
Text Fact Transfer (2023.emnlp-main)
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| Challenge: | Existing text style transfer models struggle with text fact transfer due to their inability to preserve the specificity and phrasing of the source text and tendency to hallucinate errors. |
| Approach: | They propose a task that seeks to transfer factual content between topics without changing its style. |
| Outcome: | The proposed framework can transfer factual content without sacrificing style without changing the style of the source text. |
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus (N19-1)
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| Challenge: | Existing methods for text style transfer have demonstrated considerable success, but a parallel corpus may not always be available for a transfer task. |
| Approach: | They propose a text style transfer model that uses an attention-based encoder-decoder to transfer a sentence from the source style to the target style. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two different style transfer tasks. |
“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer (D19-1)
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| Challenge: | Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. |
| Approach: | They propose a new approach to rewriting sentences to a target style in the absence of parallel style corpora by exploiting the Transformer. |
| Outcome: | The proposed method outperforms state-of-the-art systems across 5 datasets on sentiment, gender and political slant transfer. |
Domain Adaptive Text Style Transfer (D19-1)
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| Challenge: | Text style transfer without parallel data is a promising method for learning, but in the scenario where less data is available, it may yield poor performance. |
| Approach: | They propose to leverage available data to learn domain-adaptive text style transfer models . they evaluate two style transfer tasks where only limited non-parallel data is available . |
| Outcome: | The proposed models learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information and (iv) adaptively transfer the styles in a domain-aware manner. |
Neuron Activation Modulation for Text Style Transfer: Guiding Large Language Models (2025.findings-acl)
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| 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. |
Text Style Transfer with Contrastive Transfer Pattern Mining (2023.acl-long)
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| Challenge: | Existing methods for text style transfer only focus on the transformation between styles, yet they do not take into account that this transformation can be achieved via different hidden transfer patterns. |
| Approach: | They propose a novel approach which automatically mines hidden transfer patterns to improve TST . they use a clustering module to automatically discover hidden transfer pattern from the data . |
| Outcome: | The proposed method can be applied in a plug-and-play manner to enhance other methods to further improve their performance. |
Text Style Transferring via Adversarial Masking and Styled Filling (2022.emnlp-main)
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| Challenge: | Existing models for text style transfer suffer from two challenges: the word masking procedure may mistakenly remove unexpected words and the selected words in the word filling procedure lack diversity and semantic consistency. |
| Approach: | They propose a style transfer model with adversarial masking and styled filling techniques to solve these challenges. |
| Outcome: | The proposed model performs well on two benchmark text style transfer data sets. |
SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer (2024.acl-long)
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| Challenge: | Existing methods for short TST are difficult to implement and can cause content degradation. |
| Approach: | They propose a method to vary the style polarity of text while preserving semantic content. |
| Outcome: | The proposed method improves over baselines and is highly efficient. |
Engagement-driven Persona Prompting for Rewriting News Tweets (2025.coling-main)
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| Challenge: | Text style transfer is a challenging research task which modifies the linguistic style of a text to meet pre-set objectives such as making the text simpler or more accessible. |
| Approach: | They propose to use large language models to rewrite Dutch news tweets to match specific linguistic styles to achieve a more accessible and accessible text. |
| Outcome: | The proposed prompting strategies perform best for rewriting Dutch news tweets in specific linguistic styles (formal, casual and factual). |
Style-Specific Neurons for Steering LLMs in Text Style Transfer (2024.emnlp-main)
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| 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. |
A Decoupled Multi-Agent Framework for Complex Text Style Transfer (2025.findings-emnlp)
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| Challenge: | Existing models for text style transfer struggle with complex styles . existing models perform well on simple styles like sentiment and formality . |
| Approach: | They propose a multi-agent self-check framework that includes a large language model as a planner for disentangling subtasks and expert agents for executing the subtask. |
| Outcome: | The proposed framework significantly improves style strength and content preservation on simple and complex style datasets. |