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 101–150 of 186 papers

TitleStatusHype
Syntax Matters! Syntax-Controlled in Text Style Transfer—0
Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations—0
基于风格化嵌入的中文文本风格迁移(Chinese text style transfer based on stylized embedding)—0
Multi-Pair Text Style Transfer for Unbalanced Data via Task-Adaptive Meta-Learning—0
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer—0
A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow—0
Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer NormalizationCode0
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text GenerationCode0
Multi-Pair Text Style Transfer on Unbalanced Data—0
A Recipe For Arbitrary Text Style Transfer with Large Language Models—0
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer—0
Counterfactual Explanations for Survival Prediction of Cardiovascular ICU PatientsCode0
NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style TransferCode1
LEWIS: Levenshtein Editing for Unsupervised Text Style TransferCode1
A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations—0
SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer—0
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style TransferCode1
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n ParametersCode1
Civil Rephrases Of Toxic Texts With Self-Supervised TransformersCode1
GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer—0
Empirical Evaluation of Supervision Signals for Style Transfer Models—0
Parameterization of Hypercomplex Multiplications—0
Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer—0
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation—0
How Positive Are You: Text Style Transfer using Adaptive Style EmbeddingCode1
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement ApproachCode1
Deep Learning for Text Style Transfer: A SurveyCode2
DGST: a Dual-Generator Network for Text Style Transfer—0
On Learning Text Style Transfer with Direct RewardsCode0
Text Style Transfer: A Review and Experimental EvaluationCode2
Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information MaximizationCode0
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted RestylingCode1
Unsupervised Text Style Transfer with Padded Masked Language Models—0
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer—0
TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling—0
PGST: a Polyglot Gender Style Transfer methodCode0
Multimodal Text Style Transfer for Outdoor Vision-and-Language NavigationCode1
Story-level Text Style Transfer: A Proposal—0
Improving GAN Training with Probability Ratio Clipping and Sample ReweightingCode1
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance—0
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style TransferCode1
Reinforced Rewards Framework for Text Style Transfer—0
Learning Implicit Text Generation via Feature Matching—0
Review of Text Style Transfer Based on Deep Learning—0
Contextual Text Style Transfer—0
ST^2: Small-data Text Style Transfer via Multi-task Meta-Learning—0
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck—0
SentiInc: Incorporating Sentiment Information into Sentiment Transfer Without Parallel Data—0
Learning to Select Bi-Aspect Information for Document-Scale Text Content ManipulationCode0
Learning to Generate Multiple Style Transfer Outputs for an Input Sentence—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