Goal Oriented Dialogue

Goal-oriented dialogue systems aim to build conversational agents that effectively assist users in achieving specific tasks through multi-turn interactions. Current research heavily focuses on improving the planning and generation of proactive and coherent dialogue responses, often leveraging large language models (LLMs) and incorporating techniques like Monte Carlo Tree Search or reinforcement learning for policy optimization. These advancements are driven by a need for more helpful and fair systems, leading to investigations into user satisfaction estimation and the handling of unrecognized user utterances, ultimately aiming to create more robust and user-friendly conversational AI.