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
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Text Style Transfer for Bias Mitigation using Masked Language Modeling—0
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation—0
TD-ConE: An Information-Theoretic Approach to Assessing Parallel Text Generation Data—0
Rethinking Style Transformer by Energy-based Interpretation: Adversarial Unsupervised Style Transfer using Pretrained Model—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
BTS: A Bi-Lingual Benchmark for Text Segmentation in the Wild—0
VAE based Text Style Transfer with Pivot Words Enhancement LearningCode0
Multilingual pre-training with Language and Task Adaptation for Multilingual Text Style Transfer—0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Exploring Non-Autoregressive Text Style TransferCode0
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style TransferCode0
Rethinking Sentiment Style Transfer—0
Emotion Style Transfer with a Specified Intensity Using Deep Reinforcement Learning—0
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style TransferCode1
Enhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization—0
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style TransferCode0
A Review of Text Style Transfer using Deep Learning—0
Text Style Transfer with Confounders—0
Preventing Author Profiling through Zero-Shot Multilingual Back-TranslationCode0
Transductive Learning for Unsupervised Text Style TransferCode1
Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders—0
A Recipe For Arbitrary Text Style Transfer with Large Language Models—0
Unsupervised Text Style Transfer with Content Embeddings—0
Contextualizing Variation in Text Style Transfer Datasets—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