Unsupervised Text Style Transfer
21 papers with code • 3 benchmarks • 3 datasets
GYAFC
Yelp2018
Yelp
Latest papers with no code
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer
Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer.
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer
Transferring text from one domain to the other has seen tremendous progress in the recent past.
SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer
Moreover, to alleviate the conflict between the targets of the conventional denoising procedure and the style transfer task, we propose another novel style denoising mechanism, which is more compatible with the target of the style transfer task.
Unsupervised Text Style Transfer with Padded Masked Language Models
This allows us to identify the source tokens to delete to transform the source text to match the style of the target domain.
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation.
Learning Implicit Text Generation via Feature Matching
Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks.
Formality Style Transfer with Hybrid Textual Annotations
Its challenge is the lack of large-scale sentence-aligned parallel data.
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer
We introduce a new approach to tackle the problem of offensive language in online social media.
