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
Most implemented papers
Style Transfer in Text: Exploration and Evaluation
Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.
Structured Content Preservation for Unsupervised Text Style Transfer
YouzhiTian/Structured-Content-Preservation-for-Unsupervised-Text-Style-Transfer
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Text style transfer aims to modify the style of a sentence while keeping its content unchanged.
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.
Transforming Delete, Retrieve, Generate Approach for Controlled Text Style Transfer
agaralabs/transformer-drg-style-transfer
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Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information.
Text Style Transfer: A Review and Experimental Evaluation
fuzhenxin/Style-Transfer-in-Text
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This article aims to provide a comprehensive review of recent research efforts on text style transfer.
Deep Learning for Text Style Transfer: A Survey
fuzhenxin/Style-Transfer-in-Text
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CL (ACL) 2022
Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others.
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer
lvyiwei1/StylePTB
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NAACL 2021
Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e. g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence.
Studying the role of named entities for content preservation in text style transfer
Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer.
QuaSE: Accurate Text Style Transfer under Quantifiable Guidance
For example, an input sequence could be a word sequence, such as review sentence and advertisement text.
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
lancopku/unpaired-sentiment-translation
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ACL 2018
We evaluate our approach on two review datasets, Yelp and Amazon.
