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
Latest papers with no code
FAST: Font-Agnostic Scene Text Editing
However, most of the existing STE methods show inferior editing performance because of (1) complex image backgrounds, (2) various font styles, and (3) varying word lengths within the text.
Identifying the style by a qualified reader on a short fragment of generated poetry
In addition, the assessors were asked how well they were familiar with the work of the poet they had named.
A Call for Standardization and Validation of Text Style Transfer Evaluation
Text Style Transfer (TST) evaluation is, in practice, inconsistent.
Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer
This paper explores the impact of training data input diversity on the quality of the generated text from the multi-style transfer model.
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer
Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the significant amount of computational resources and labeled data required for the specific task.
Conversation Style Transfer using Few-Shot Learning
Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e. g., formality).
SimpleStyle: An Adaptable Style Transfer Approach
We offer our protocol as a simple yet strong baseline for works that wish to make incremental advancements in the field of attribute controlled text rewriting.
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer
Since cycle construction helps to improve the style transfer ability of the model by rebuilding transferred sentences back to original-style sentences, it brings about a content loss in unsupervised text style transfer tasks.
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation
In this work, we study the usefulness of Abstract Meaning Representation (AMR) graph as the intermediate style agnostic representation.
On Text Style Transfer via Style Masked Language Models
Text Style Transfer (TST) is performable through approaches such as latent space disentanglement, cycle-consistency losses, prototype editing etc.
