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 76–100 of 186 papers

TitleStatusHype
Are Large Language Models Actually Good at Text Style Transfer?Code0
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style TransferCode0
Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information MaximizationCode0
Human Judgement as a Compass to Navigate Automatic Metrics for Formality TransferCode0
Identifying the style by a qualified reader on a short fragment of generated poetryCode0
Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-TrainingCode0
Domain Adaptive Text Style TransferCode0
STEER: Unified Style Transfer with Expert ReinforcementCode0
Structured Content Preservation for Unsupervised Text Style TransferCode0
Studying the role of named entities for content preservation in text style transferCode0
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patientsCode0
Style Transfer for Texts: Retrain, Report Errors, Compare with RewritesCode0
Style Transfer from Non-Parallel Text by Cross-AlignmentCode0
Style Transfer in Text: Exploration and EvaluationCode0
Style Transfer Through Back-TranslationCode0
Style Transfer with Multi-iteration Preference OptimizationCode0
Don't lose the message while paraphrasing: A study on content preserving style transferCode0
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style TransferCode0
Text Detoxification as Style Transfer in English and HindiCode0
Zero-Shot Fine-Grained Style Transfer: Leveraging Distributed Continuous Style Representations to Transfer To Unseen Styles—0
A Call for Standardization and Validation of Text Style Transfer Evaluation—0
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer—0
A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow—0
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation—0
A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations—0
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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