A Task-Oriented Multi-turn Dialogue Mechanism for the Smart Cockpit

As an important carrier and platform for AI applications, the dialogue system for smart cockpits will become an important scenario for AI applications. However, there are some problems with the existing smart cockpit dialogue system, such as a lack of relevant corpus and knowledge annotations. These problems lead to the low accuracy of response representation generated by task-oriented dialogue systems for smart cockpits, which cannot meet the needs of smart human-computer interaction. To this end, this paper designs cockpitWOZ, a multi-round Chinese conversation dataset with knowledge annotation in the smart cockpit domain, which contains 2.9k conversations in four domains, including restaurants, attractions, music, and itineraries. On this basis, a new knowledge-driven task-based dialogue model is designed in this paper based on multiple baseline models. Firstly, a knowledge graph named cockpitKG based on the smart cockpit scenario is constructed to enhance the responsiveness of the model. Secondly, the smart cockpit system is built and a label replacement method is used to meet the requirements for real-time interaction during vehicle movement. Finally, the experimental results show that this model outperforms the baseline model and demonstrates that the introduction of background knowledge and label replacement can lead to higher-quality responses in the smart cockpit dialogue system.