Dialogue Understanding

Dialogue understanding focuses on enabling computers to comprehend the nuances of human conversation, aiming to accurately interpret meaning, intent, and context within multi-turn interactions. Current research heavily utilizes large language models (LLMs), often employing techniques like prompt engineering, multi-task learning, and contextualized representations (e.g., incorporating future dialogue turns or semantic structures like AMR graphs) to improve performance across various dialogue tasks, including state tracking and summarization. Advances in this field are crucial for developing more natural and effective human-computer interaction in applications ranging from conversational AI assistants to medical diagnosis support systems.