Dialogue Data Augmentation

Dialogue data augmentation focuses on artificially expanding limited conversational datasets to improve the performance of dialogue systems, particularly in low-resource scenarios or specialized domains like psychological counseling. Current research emphasizes leveraging large language models to generate diverse and contextually relevant dialogue turns, often incorporating techniques like style transfer, knowledge integration, and simulation of real-world conversational nuances such as speech errors. This work is crucial for advancing conversational AI, enabling the development of more robust, adaptable, and inclusive dialogue systems across various applications, from task-oriented assistants to emotionally supportive chatbots.