Year: 2026
Type: conference-paper
Abstract: Multi-domain Dialogue State Tracking (DST)requires discourse coherence that transcends independent slot-filling.Most existing approaches rely on statistical regularities within static schemas, failing to capture the semantic coordination governing simultaneous slot updates.In this paper, we propose Event-DST, which models latent events as cognitive organizing units to dynamically coordinate slot interactions.By projecting dialogue context into a continuous semantic space, our model induces a dynamic structural bias to enforce pragmatic consistency.This structural guidance is integrated via a dual-stream fusion strategy that balances top-down structural constraints with bottom-up textual precision.Experimental results on two benchmarks demonstrate the superiority of our framework, providing an interpretable and parameter-efficient path toward robust dialogue understanding.
Institution: Shanghai University
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166805795
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