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 26–50 of 186 papers

TitleStatusHype
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
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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