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 126–150 of 186 papers

TitleStatusHype
Unsupervised Text Style Transfer with Deep Generative Models—0
Unsupervised Text Style Transfer with Padded Masked Language Models—0
XL-Editor: Post-editing Sentences with XLNet—0
On Variational Learning of Controllable Representations for Text without SupervisionCode0
Unsupervised Text Style Transfer using Language Models as DiscriminatorsCode0
ALTER: Auxiliary Text Rewriting Tool for Natural Language GenerationCode0
VAE based Text Style Transfer with Pivot Words Enhancement LearningCode0
Controllable Artistic Text Style Transfer via Shape-Matching GANCode0
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text GenerationCode0
Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-TrainingCode0
Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style TransferCode0
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text GenerationCode0
STEER: Unified Style Transfer with Expert ReinforcementCode0
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style TransferCode0
Don't lose the message while paraphrasing: A study on content preserving style transferCode0
Learning Sentiment Memories for Sentiment Modification without Parallel DataCode0
Learning to Select Bi-Aspect Information for Document-Scale Text Content ManipulationCode0
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style TransferCode0
Structured Content Preservation for Unsupervised Text Style TransferCode0
Studying the role of named entities for content preservation in text style transferCode0
Are Large Language Models Actually Good at Text Style Transfer?Code0
Learning Evaluation Models from Large Language Models for Sequence GenerationCode0
MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style TransferCode0
Domain Adaptive Text Style TransferCode0
Multidimensional Evaluation for Text Style Transfer Using ChatGPTCode0
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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