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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 of 186 papers

TitleStatusHype
Text Style Transfer: A Review and Experimental EvaluationCode2
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement LearningCode2
Deep Learning for Text Style Transfer: A SurveyCode2
Multimodal Text Style Transfer for Outdoor Vision-and-Language NavigationCode1
Fine-grained Text Style Transfer with Diffusion-Based Language ModelsCode1
Multiple-Attribute Text Style TransferCode1
Transductive Learning for Unsupervised Text Style TransferCode1
Prompt-Based Editing for Text Style TransferCode1
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style TransferCode1
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship EmbeddingsCode1
Civil Rephrases Of Toxic Texts With Self-Supervised TransformersCode1
Composable Text Controls in Latent Space with ODEsCode1
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style TransferCode1
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n ParametersCode1
IT5: Text-to-text Pretraining for Italian Language Understanding and GenerationCode1
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent RepresentationCode1
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style TransferCode1
NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style TransferCode1
So Different Yet So Alike! Constrained Unsupervised Text Style TransferCode1
StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content EnhancingCode1
Style-Specific Neurons for Steering LLMs in Text Style TransferCode1
A Probabilistic Formulation of Unsupervised Text Style TransferCode1
WAS: Dataset and Methods for Artistic Text SegmentationCode1
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and TranslationCode1
Improving GAN Training with Probability Ratio Clipping and Sample ReweightingCode1
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted RestylingCode1
A large-scale computational study of content preservation measures for text style transfer and paraphrase generationCode1
CAT-LLM: Prompting Large Language Models with Text Style Definition for Chinese Article-style TransferCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Learning to Model Editing ProcessesCode1
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement ApproachCode1
How Positive Are You: Text Style Transfer using Adaptive Style EmbeddingCode1
Inducing Positive Perspectives with Text ReframingCode1
Transforming Delete, Retrieve, Generate Approach for Controlled Text Style TransferCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
LEWIS: Levenshtein Editing for Unsupervised Text Style TransferCode1
A Review of Text Style Transfer using Deep Learning—0
Conversation Style Transfer using Few-Shot Learning—0
Grammatical Error Correction and Style Transfer via Zero-shot Monolingual Translation—0
Contextual Text Style Transfer—0
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer—0
GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer—0
Contextualizing Variation in Text Style Transfer Datasets—0
A Recipe For Arbitrary Text Style Transfer with Large Language Models—0
A Recipe For Arbitrary Text Style Transfer with Large Language Models—0
Emotion Style Transfer with a Specified Intensity Using Deep Reinforcement Learning—0
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation—0
A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow—0
Empirical Evaluation of Supervision Signals for Style Transfer Models—0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Show:102550
← PrevPage 1 of 4Next →
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