dialoguemanagement

Dialoguemanagement is the part of a dialogue system that decides the system's next action based on the conversation history and goals. In task-oriented systems it coordinates language understanding, dialogue state tracking, action selection, and natural language generation to achieve user tasks.

Core concepts include the dialogue state or belief state, which encodes user intents and slots, and the policy, which maps states to system actions. Dialogue state tracking updates belief as new observations arrive. The action space includes asking clarifying questions, calling backend services, proposing answers, or ending the turn. NLG renders the chosen action as natural language.

Architectures range from modular pipelines with separate DST, policy, and NLG modules to end-to-end neural models that learn a latent policy and may use implicit state representations.

Techniques encompass rule-based and frame-based approaches, reinforcement learning for policy optimization, supervised learning from dialogue data, imitation learning, and planning-based methods.

Evaluation uses task success, dialogue efficiency, user satisfaction, and objective measures such as slot accuracy or error rates.

Challenges include robust state tracking under ASR and noise, user goal evolution, handling multi-domain conversations, and ensuring transparency and safety. Datasets and benchmarks for task-oriented dialogue support evaluation across domains, enabling comparisons of approaches and progress over time.

Future trends involve hybrid systems that combine explicit state representations with neural policies, improved transfer across domains, and better handling of open-ended or mixed-initiative interactions.