A Survey of Text Style Transfer: Applications and Ethical Implications
Not on the map. Why?
Abstract (arXiv)
Text style transfer (TST) is an important task in controllable text generation, which aims to control selected attributes of language use, such as politeness, formality, or sentiment, without altering the style-independent content of the text. The field has received considerable research attention in recent years and has already been covered in several reviews, but the focus has mostly been on the development of new algorithms and learning from different types of data (supervised, unsupervised, out-of-domain, etc.) and not so much on the application side. However, TST-related technologies are gradually reaching a production- and deployment-ready level, and therefore, the inclusion of the application perspective in TST research becomes crucial. Similarly, the often overlooked ethical considerations of TST technology have become a pressing issue. This paper presents a comprehensive review of TST applications that have been researched over the years, using both traditional linguistic approaches and more recent deep learning methods. We discuss current challenges, future research directions, and ethical implications of TST applications in text generation. By providing a holistic overview of the landscape of TST applications, we hope to stimulate further research and contribute to a better understanding of the potential as well as ethical considerations associated with TST.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 91 of its 198 references that are in the map. First posted on arXiv 23 Jul 2024.
Builds on
91 of 198 references.
References · 91
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2019How Can We Know What Language Models Know?cited by 953
- 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
- 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
- 2020Recipes for building an open-domain chatbotcited by 593
- 2015A Neural Algorithm of Artistic Stylecited by 480
38 more
- 2017Toward Controlled Generation of Textcited by 271
- 2017Style Transfer from Non-Parallel Text by Cross-Alignmentcited by 246
- 2016A Persona-Based Neural Conversation Modelcited by 221
- 2018Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfercited by 209
- 2017Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluationcited by 206
- 2018Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfercited by 191
- 2021Reframing Instructional Prompts to GPTk's Languagecited by 176
- 2017A Survey on Dialogue Systems: Recent Advances and New Frontierscited by 162
- 2020Deep Learning for Text Style Transfer: A Surveycited by 161
- 2017Learning to Generate Reviews and Discovering Sentimentcited by 147
- 2021Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfercited by 147
- 2017Style Transfer in Text: Exploration and Evaluationcited by 145
- 2018Disentangled Representation Learning for Non-Parallel Text Style Transfercited by 127
- 2018Style Transfer Through Back-Translationcited by 124
- 2017Understanding Abuse: A Typology of Abusive Language Detection Subtaskscited by 115
- 2021A Recipe For Arbitrary Text Style Transfer with Large Language Modelscited by 107
- 2019Automatically Neutralizing Subjective Bias in Textcited by 105
- 2019Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representationcited by 84
- 2018Style Augmentation: Data Augmentation via Style Randomizationcited by 79
- 2024A Design Space for Intelligent and Interactive Writing Assistantscited by 76
- 2021Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approachcited by 75
- 2021BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Datacited by 75
- 2019A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Datacited by 74
- 2017The spread of low-credibility content by social botscited by 73
- 2021A Practical Survey on Faster and Lighter Transformerscited by 73
- 2020A Probabilistic Formulation of Unsupervised Text Style Transfercited by 70
- 2019A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfercited by 65
- 2017Text Summarization Techniques: A Brief Surveycited by 59
- 2019Evaluating Style Transfer for Textcited by 59
- 2020PowerTransformer: Unsupervised Controllable Revision for Biased Language Correctioncited by 59
- 2017Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Modelscited by 57
- 2018Polite Dialogue Generation Without Parallel Datacited by 57
- 2018Fighting Offensive Language on Social Media with Unsupervised Text Style Transfercited by 55
- 2022Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Modelscited by 49
- 2015SentiCap: Generating Image Descriptions with Sentimentscited by 48
- 2020Hooks in the Headline: Learning to Generate Headlines with Controlled Stylescited by 46
- 2021G-Transformer for Document-level Machine Translationcited by 46
- 2020Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymencited by 45
50 most cited of 91 All 91 (JSON)
Built on by
Not cited yet.