Text Style Transfer
80 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
CAT-LLM: Prompting Large Language Models with Text Style Definition for Chinese Article-style Transfer
Text style transfer is increasingly prominent in online entertainment and social media.
STEER: Unified Style Transfer with Expert Reinforcement
shallinan1/steerstyletransfer
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We propose STEER: Unified Style Transfer with Expert Reinforcement, a unified frame-work developed to overcome the challenge of limited parallel data for style transfer.
Text Fact Transfer
nbalepur/text-fact-transfer
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Text style transfer is a prominent task that aims to control the style of text without inherently changing its factual content.
Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-Training
In this work, we introduce the concept of complex text style transfer tasks, and constructed complex text datasets based on two widely applicable scenarios.
Don't lose the message while paraphrasing: A study on content preserving style transfer
Text style transfer techniques are gaining popularity in natural language processing allowing paraphrasing text in the required form: from toxic to neural, from formal to informal, from old to the modern English language, etc.
MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer
oldbirdaz/mssrnet_style_transfer
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Our proposed method addresses this issue by assigning individual style vector to each token in a text, allowing for fine-grained control and manipulation of the style strength.
Text Style Transfer Back-Translation
frxxzhl/ssebt
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To address this issue, we propose Text Style Transfer Back Translation (TST BT), which uses a style transfer model to modify the source side of BT data.
Fine-grained Text Style Transfer with Diffusion-Based Language Models
lvyiwei1/diffuseq_styleptb
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Diffusion probabilistic models have shown great success in generating high-quality images controllably, and researchers have tried to utilize this controllability into text generation domain.
Multidimensional Evaluation for Text Style Transfer Using ChatGPT
laihuiyuan/eval-formality-transfer
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We investigate the potential of ChatGPT as a multidimensional evaluator for the task of \emph{Text Style Transfer}, alongside, and in comparison to, existing automatic metrics as well as human judgements.
Prompt-Based Editing for Text Style Transfer
In this paper, we present a prompt-based editing approach for text style transfer.
