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
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