| 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. |
Similar Papers
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 . |
Text Style Transfer Back-Translation (2023.acl-long)
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Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
| Challenge: | Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task. |
| Approach: | They propose a method to modify the style of inputs by modifying the source side of BT data. |
| Outcome: | The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs. |
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. |
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. |
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer (2023.findings-emnlp)
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| Challenge: | Existing studies explore performing text style transfer on attributes like age, gender, formality, politeness, and formality. |
| Approach: | They propose a framework that freezes the pre-trained model’s original parameters and enables the development of a multiple-attribute text style transfer model. |
| Outcome: | The proposed model outperforms state-of-the-art models on sentiment transfer and multiple-attribute transfer tasks with significantly less computational resources. |
AMR-TST: Abstract Meaning Representation-based Text Style Transfer (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. |
| Approach: | They propose an AMR-based text style transfer technique that converts source text to an AML graph and generates transferred text based on the AMR graph modified by a TST policy named style rewriting. |
| Outcome: | The proposed method achieves state-of-the-art results compared with baseline models in automatic and human evaluations. |
Semi-supervised Text Style Transfer: Cross Projection in Latent Space (D19-1)
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| Challenge: | Text style transfer task has long suffered from the shortage of parallel data . |
| Approach: | They propose a semi-supervised text style transfer model that combines parallel data with large-scale nonparallel data to train it. |
| Outcome: | The proposed model can transfer a sentence of one style to another while retaining its original content meaning while preserving its original meaning. |
Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization (2020.findings-emnlp)
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| Challenge: | Formality style transfer is the task of converting informal sentences to grammatically-correct formal sentences. |
| Approach: | They propose a semi-supervised formality style transfer model that utilizes a language model-based discriminator to maximize the likelihood of the output sentence being formal. |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of automated metrics and human judgement. |
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. |
A Call for Standardization and Validation of Text Style Transfer Evaluation (2023.findings-acl)
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| Challenge: | Text style transfer (TST) evaluation is inconsistent in practice. |
| Approach: | They conduct a meta-analysis on human and automated TST evaluation and experimentation . they find a standardization gap and a validation gap in the field . |
| Outcome: | The authors find that evaluation procedures are inconsistent and that they need to improve on them. |