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
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
Grammatical Error Correction and Style Transfer via Zero-shot Monolingual Translation—0
GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer—0
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer—0
How Sequence-to-Sequence Models Perceive Language Styles?—0
Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mapping—0
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance—0
Improving Long Text Understanding with Knowledge Distilled from Summarization Model—0
基于风格化嵌入的中文文本风格迁移(Chinese text style transfer based on stylized embedding)—0
Learning Implicit Text Generation via Feature Matching—0
Learning to Generate Multiple Style Transfer Outputs for an Input Sentence—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
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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