SOTAVerified

Text Style Transfer

Text Style Transfer is the task of controlling certain attributes of generated text. The state-of-the-art methods can be categorized into two main types which are used on parallel and non-parallel data. Methods on parallel data are typically supervised methods that use a neural sequence-to-sequence model with the encoder-decoder architecture. Methods on non-parallel data are usually unsupervised approaches using Disentanglement, Prototype Editing and Pseudo-Parallel Corpus Construction.

The popular benchmark for this task is the Yelp Review Dataset. Models are typically evaluated with the metrics of Sentiment Accuracy, BLEU, and PPL.

Papers

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Showing 26–50 of 186 papers

TitleStatusHype
How Positive Are You: Text Style Transfer using Adaptive Style EmbeddingCode1
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement ApproachCode1
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted RestylingCode1
Multimodal Text Style Transfer for Outdoor Vision-and-Language NavigationCode1
Improving GAN Training with Probability Ratio Clipping and Sample ReweightingCode1
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style TransferCode1
A Probabilistic Formulation of Unsupervised Text Style TransferCode1
Transforming Delete, Retrieve, Generate Approach for Controlled Text Style TransferCode1
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent RepresentationCode1
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and TranslationCode1
Multiple-Attribute Text Style TransferCode1
Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mapping—0
Evaluating Text Style Transfer Evaluation: Are There Any Reliable Metrics?—0
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing—0
Multi-Attribute Constraint Satisfaction via Language Model Rewriting—0
Multilingual and Explainable Text Detoxification with Parallel CorporaCode0
A Survey of Text Style Transfer: Applications and Ethical Implications—0
SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning—0
Text Style Transfer: An Introductory Overview—0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
Change My Frame: Reframing in the Wild in r/ChangeMyView—0
Style Transfer with Multi-iteration Preference OptimizationCode0
Out of style: Misadventures with LLMs and code style transfer—0
Are Large Language Models Actually Good at Text Style Transfer?Code0
SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style TransferCode0
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All datasetsYelp Review Dataset (Small)Yelp Review Dataset (Large)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SAE+DiscriminatorG-Score (BLEU, Accuracy)74.56—Unverified
2LatentOps (Few shot)G-Score (BLEU, Accuracy)71.6—Unverified
3SentiIncG-Score (BLEU, Accuracy)66.25—Unverified
4DeleteAndRetrieveG-Score (BLEU, Accuracy)54.64—Unverified
5DeleteOnlyG-Score (BLEU, Accuracy)54.11—Unverified
6MultiDecoderG-Score (BLEU, Accuracy)45.02—Unverified
7CAEG-Score (BLEU, Accuracy)38.66—Unverified
8StyleEmbeddingG-Score (BLEU, Accuracy)31.31—Unverified
#ModelMetricClaimedVerifiedStatus
1SentiIncG-Score (BLEU, Accuracy)59.17—Unverified
2StyleEmbBLEU30—Unverified