Text Style Transfer

Text style transfer (TST) aims to change the style of text (e.g., formality, sentiment, authorship) while preserving its meaning. Current research emphasizes improving content preservation and style consistency, particularly for longer texts, using various approaches including transformer-based models, diffusion models, and techniques like back-translation and reinforcement learning. These advancements are driving progress in applications such as chatbot development, text detoxification, and cross-lingual communication, while also highlighting the need for standardized evaluation metrics and addressing ethical concerns surrounding potential misuse.