Text Style Transfer
81 papers with code • 2 benchmarks • 6 datasets
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.
Benchmarks
These leaderboards are used to track progress in Text Style Transfer
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Libraries
Use these libraries to find Text Style Transfer models and implementations
Yelp
Touchdown Dataset
TextSeg
StylePTB
XFORMAL
TextBox 2.0
Most implemented papers
Unsupervised Text Style Transfer using Language Models as Discriminators
Binary classifiers are often employed as discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain.
Learning Sentiment Memories for Sentiment Modification without Parallel Data
The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content.
Controllable Artistic Text Style Transfer via Shape-Matching GAN
TAMU-VITA/ShapeMatchingGAN
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ICCV 2019
In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter.
On Variational Learning of Controllable Representations for Text without Supervision
BorealisAI/CP-VAE
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ICML 2020
The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space.
Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
dayihengliu/Fine-Grained-Style-Transfer
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We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer.
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
ChenWu98/Point-Then-Operate
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ACL 2019
Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision.
Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
Second, starting with certain values of bilingual evaluation understudy (BLEU) between input and output and accuracy of the sentiment transfer the optimization of these two standard metrics diverge from the intuitive goal of the style transfer task.
Domain Adaptive Text Style Transfer
cookielee77/DAST
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IJCNLP 2019
These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training.
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation
xuqiongkai/ALTER
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IJCNLP 2019
In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewriting, and text style transfer.
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
syw1996/SCIR-TG-Data2text-Bi-Aspect
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In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.
