Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking
Zero-shot learning for Dialogue State Tracking (DST) focuses on generalizing to an unseen domain without the expense of collecting in domain data. However, previous zero-shot DST methods ignore the slot dependencies in a multidomain dialogue, resulting in sub-optimal performances when adapting to unseen domains. In this paper, we utilize slot prompts combination, slot values demonstration, and slot constraint object to model the slot-slot dependencies, slot-value dependency and slot-context dependency respectively. Specifically, each slot prompt consists of a slot-specific prompt and a slot-shared prompt to capture the shared knowledge across different domains. Experimental results show the effectiveness of our proposed method over existing state-of-art generation methods under zero-shot/few-shot settings.
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Dialogue State TrackingZero-Shot LearningMethods 이 논문이 사용한 방법론
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