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
Style-transfer counterfactual explanations: An application to mortality prevention of ICU patientsCode0
SimpleStyle: An Adaptable Style Transfer Approach—0
Pay Attention to Your Tone: Introducing a New Dataset for Polite Language RewriteCode0
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer—0
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation—0
Replacing Language Model for Style TransferCode0
On Text Style Transfer via Style Masked Language Models—0
GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation—0
Text Style Transfer via Optimal Transport—0
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text GenerationCode0
Studying the role of named entities for content preservation in text style transferCode0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style TransferCode0
Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style TransferCode0
A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification—0
Non-Parallel Text Style Transfer with Self-Parallel SupervisionCode0
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation—0
Human Judgement as a Compass to Navigate Automatic Metrics for Formality TransferCode0
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style TransferCode0
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language GenerationCode0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Text Style Transfer for Bias Mitigation using Masked Language Modeling—0
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Rethinking Style Transformer by Energy-based Interpretation: Adversarial Unsupervised Style Transfer using Pretrained Model—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