Dialogue Structure

Dialogue structure research aims to automatically understand and represent the underlying organization of conversations, improving both our comprehension of human interaction and the design of more natural and coherent dialogue systems. Current efforts focus on developing unsupervised and semi-supervised methods, often leveraging transformer-based architectures like BERT and contrastive learning, to extract structural information from both task-oriented and open-domain dialogues, frequently represented as graphs or state transition models. These advancements enable more effective dialogue generation, improved data augmentation techniques, and a deeper understanding of conversational dynamics, ultimately leading to more sophisticated and human-like conversational agents.