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 51–100 of 186 papers

TitleStatusHype
Replacing Language Model for Style TransferCode0
On Text Style Transfer via Style Masked Language Models—0
StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content EnhancingCode1
Composable Text Controls in Latent Space with ODEsCode1
GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation—0
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text GenerationCode0
Text Style Transfer via Optimal Transport—0
Studying the role of named entities for content preservation in text style transferCode0
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement LearningCode2
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Learning to Model Editing ProcessesCode1
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style TransferCode0
So Different Yet So Alike! Constrained Unsupervised Text Style TransferCode1
Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style TransferCode0
A large-scale computational study of content preservation measures for text style transfer and paraphrase generationCode1
A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification—0
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
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
Inducing Positive Perspectives with Text ReframingCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style TransferCode0
IT5: Text-to-text Pretraining for Italian Language Understanding and GenerationCode1
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
TD-ConE: An Information-Theoretic Approach to Assessing Parallel Text Generation Data—0
Rethinking Style Transformer by Energy-based Interpretation: Adversarial Unsupervised Style Transfer using Pretrained Model—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
BTS: A Bi-Lingual Benchmark for Text Segmentation in the Wild—0
VAE based Text Style Transfer with Pivot Words Enhancement LearningCode0
Multilingual pre-training with Language and Task Adaptation for Multilingual Text Style Transfer—0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Exploring Non-Autoregressive Text Style TransferCode0
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style TransferCode0
Rethinking Sentiment Style Transfer—0
Emotion Style Transfer with a Specified Intensity Using Deep Reinforcement Learning—0
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style TransferCode1
Enhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization—0
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style TransferCode0
A Review of Text Style Transfer using Deep Learning—0
Text Style Transfer with Confounders—0
Preventing Author Profiling through Zero-Shot Multilingual Back-TranslationCode0
Transductive Learning for Unsupervised Text Style TransferCode1
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders—0
A Recipe For Arbitrary Text Style Transfer with Large Language Models—0
Unsupervised Text Style Transfer with Content Embeddings—0
Contextualizing Variation in Text Style Transfer Datasets—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