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 76–100 of 186 papers

TitleStatusHype
Evaluating Text Style Transfer Evaluation: Are There Any Reliable Metrics?—0
Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification—0
FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework—0
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer—0
Formality Style Transfer with Hybrid Textual Annotations—0
GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation—0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
LMStyle Benchmark: Evaluating Text Style Transfer for Chatbots—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Multi-Attribute Constraint Satisfaction via Language Model Rewriting—0
Multilingual pre-training with Language and Task Adaptation for Multilingual Text Style Transfer—0
Multi-Pair Text Style Transfer for Unbalanced Data via Task-Adaptive Meta-Learning—0
Multi-Pair Text Style Transfer on Unbalanced Data—0
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training—0
Neural Text Style Transfer via Denoising and Reranking—0
On Text Style Transfer via Style Masked Language Models—0
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
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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