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 151–175 of 186 papers

TitleStatusHype
Out of style: Misadventures with LLMs and code style transfer—0
Parameterization of Hypercomplex Multiplications—0
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing—0
Prefix-Tuning Based Unsupervised Text Style Transfer—0
Reinforced Rewards Framework for Text Style Transfer—0
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus—0
Rethinking Sentiment Style Transfer—0
Rethinking Style Transformer by Energy-based Interpretation: Adversarial Unsupervised Style Transfer using Pretrained Model—0
Review of Text Style Transfer Based on Deep Learning—0
Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer—0
SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer—0
Semi-supervised Text Style Transfer: Cross Projection in Latent Space—0
SentiInc: Incorporating Sentiment Information into Sentiment Transfer Without Parallel Data—0
SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning—0
SimpleStyle: An Adaptable Style Transfer Approach—0
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer—0
ST^2: Small-data Text Style Transfer via Multi-task Meta-Learning—0
Story-level Text Style Transfer: A Proposal—0
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer—0
Syntax Matters! Syntax-Controlled in Text Style Transfer—0
TD-ConE: An Information-Theoretic Approach to Assessing Parallel Text Generation Data—0
TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling—0
Text Style Transfer: An Introductory Overview—0
Text Style Transfer Evaluation Using Large Language Models—0
Text Style Transfer for Bias Mitigation using Masked Language Modeling—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