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 51–75 of 186 papers

TitleStatusHype
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation—0
Evaluating Text Style Transfer Evaluation: Are There Any Reliable Metrics?—0
A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow—0
Contextualizing Variation in Text Style Transfer Datasets—0
Multi-Pair Text Style Transfer on Unbalanced Data—0
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer—0
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation—0
Formality Style Transfer with Hybrid Textual Annotations—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—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
Change My Frame: Reframing in the Wild in r/ChangeMyView—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
BTS: A Bi-Lingual Benchmark for Text Segmentation in the Wild—0
A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations—0
Distilling Text Style Transfer With Self-Explanation From LLMs—0
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders—0
A Call for Standardization and Validation of Text Style Transfer Evaluation—0
Multi-Attribute Constraint Satisfaction via Language Model Rewriting—0
Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer—0
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck—0
DGST: a Dual-Generator Network for Text Style Transfer—0
Improving Long Text Understanding with Knowledge Distilled from Summarization Model—0
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation—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