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 101–125 of 186 papers

TitleStatusHype
A Recipe For Arbitrary Text Style Transfer with Large Language Models—0
A Recipe For Arbitrary Text Style Transfer with Large Language Models—0
A Review of Text Style Transfer using Deep Learning—0
A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification—0
A Survey of Text Style Transfer: Applications and Ethical Implications—0
Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer—0
BTS: A Bi-Lingual Benchmark for Text Segmentation in the Wild—0
Change My Frame: Reframing in the Wild in r/ChangeMyView—0
Contextualizing Variation in Text Style Transfer Datasets—0
Contextual Text Style Transfer—0
Conversation Style Transfer using Few-Shot Learning—0
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer—0
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer—0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
DGST: a Dual-Generator Network for Text Style Transfer—0
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck—0
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders—0
Distilling Text Style Transfer With Self-Explanation From LLMs—0
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation—0
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation—0
Emotion Style Transfer with a Specified Intensity Using Deep Reinforcement Learning—0
Empirical Evaluation of Supervision Signals for Style Transfer Models—0
Enhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization—0
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
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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