A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding

Dialogue State Tracking (DST) is crucial for understanding user needs and executing appro- priate system actions in task-oriented dialogues. Majority of existing DST methods are designed to work within predefined ontologies and as- sume the availability of gold domain labels, struggling with adapting to new slots values. While Large Language Models (LLMs)-based systems show promising zero-shot DST perfor- mance, they either require extensive computa- tional resources or they underperform existing fully-trained systems, limiting their practical- ity. To address these limitations, we propose a zero-shot, open-vocabulary system that in- tegrates domain classification and DST in a single pipeline. Our approach includes refor- mulating DST as a question-answering task for less capable models and employing self- refining prompts for more adaptable ones. Our system does not rely on fixed slot values de- fined in the ontology allowing the system to adapt dynamically. We compare our approach with existing SOTA, and show that it provides up to 20% better Joint Goal Accuracy (JGA) over previous methods on datasets like Multi- WOZ 2.1, with up to 90% fewer requests to the LLM API.