Unsupervised Text Style Transfer

Unsupervised text style transfer aims to automatically change the style of a sentence (e.g., formal to informal) without altering its meaning, a challenging task due to the lack of parallel data showing corresponding sentences in different styles. Current research heavily utilizes large language models (LLMs) and generative adversarial networks (GANs), often incorporating techniques like attention masking, prefix-tuning, and disentangled latent representations to improve style control and content preservation. These advancements hold significant potential for applications such as data augmentation, improving user experience in conversational AI, and mitigating biases in text data.