Papers with Style transfer
Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer (N18-1)
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| 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 . |
Style Transfer Through Back-Translation (P18-1)
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| Challenge: | a new method for automatic style transfer is proposed to preserve the meaning of the text while reducing stylistic properties. |
| Approach: | They propose a method for automatic style transfer that uses latent representations of the input sentence to preserve meaning while reducing stylistic properties. |
| Outcome: | The proposed method improves on sentiment, gender and political slant styles on three different styles. |
Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus (2021.naacl-main)
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| Challenge: | Existing methods for style transfer require joint annotations across all stylistic dimensions, limiting their application to multiple styles. |
| Approach: | They initialize an encoder-decoder setup with transformer-based language model pre-trained on a generic corpus and enhance its re-writing capability to multiple target style dimensions by employing multiple style-aware language models as discriminators. |
| Outcome: | The proposed model can control styles across multiple style dimensions while preserving content of the input text. |
Generic resources are what you need: Style transfer tasks without task-specific parallel training data (2021.emnlp-main)
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| Challenge: | Text style transfer is a task aimed at converting a text of one style into another while preserving its content. |
| Approach: | They propose a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model and an iterative back-translation approach to train two models in a transfer direction. |
| Outcome: | The proposed method outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. |
Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings (2022.acl-long)
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| Challenge: | Existing methods for few-shot style transfer often copy inputs verbatim . a new method is better at controlling the style transfer magnitude using an input scalar knob. |
| Approach: | They propose a method to model the stylistic difference between paraphrases by rewriting a sentence into a target style while preserving semantics. |
| Outcome: | The proposed method achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages. |
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation (2021.emnlp-main)
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| Challenge: | Documents as short as a single sentence may reveal sensitive information about authors . style transfer is effective but a number of current methods cause a drop in down-stream utility . |
| Approach: | They propose a method to remove sensitive information from documents by multilingual back-translation using off-the-shelf translation models. |
| Outcome: | The proposed method lowers adversarial gender and race prediction by 22% while retaining 95% of original utility on downstream tasks. |
Colloquial Singaporean English Style Transfer with Fine-Grained Explainable Control (2025.acl-long)
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| Challenge: | Existing methods for style transfer between Singlish and Standard English lack explainability and fine-grained control. |
| Approach: | They propose a multi-agent framework where large language models act as expert agents for each linguistic aspect. |
| Outcome: | The proposed model enables precise, interpretable transformations, advancing explainability in NLP for Singlish. |