multiturn

Multiturn refers to a dialogue or interaction that unfolds over multiple turns between participants, typically a user and a system or agent. In contrast to single-turn interactions, multiturn dialogues require maintaining and using context across exchanges to interpret user intent, resolve ambiguity, and provide coherent, contextually appropriate responses.

In natural language processing and human-computer interaction, multiturn dialogue systems aim to manage state across turns. They usually comprise modules for natural language understanding, dialogue state tracking, dialogue management, and natural language generation, along with access to external knowledge bases or databases. The system maintains a belief state or slot-value memory representing progress toward a user goal, updating intents, slots, and dialogue goals as the conversation advances and selecting actions or responses accordingly. Data-driven approaches often use neural architectures with memory components, and may employ reinforcement learning to optimize long-term task success. Datasets such as MultiWOZ and Taskmaster are commonly used for training and evaluation.

Challenges in multiturn dialogue include maintaining long-range context, resolving coreference and ellipsis, accurate slot filling across turns, and avoiding error propagation from early exchanges. Additional concerns involve latency, scalability, privacy, and evaluating progress toward user goals. Approaches range from rule-based dialogue managers to end-to-end neural systems, with hybrid methods aiming to balance interpretability and performance.

Applications of multiturn dialogue systems are widespread, including customer service chatbots, virtual assistants, voice-activated devices, and tutoring programs. Evaluation typically combines task success or goal completion with measures of user satisfaction, dialogue efficiency, and sometimes human judgment to assess coherence and helpfulness.