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        <title>Event-Guided Slot Interaction for Multi-Domain Dialogue State Tracking</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Event-Guided Slot Interaction for Multi-Domain Dialogue State Tracking
%A Xia, Ying
%A Liu, Wei
%Y Bonial, Claire
%Y Berzak, Yevgeni
%S Proceedings of the 30th Conference on Computational Natural Language Learning
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-410-1
%F xia-liu-2026-event
%X 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.
%U https://aclanthology.org/2026.conll-main.30/
%P 515-525

Markdown (Informal)

[Event-Guided Slot Interaction for Multi-Domain Dialogue State Tracking](https://aclanthology.org/2026.conll-main.30/) (Xia & Liu, CoNLL 2026)

• Event-Guided Slot Interaction for Multi-Domain Dialogue State Tracking (Xia & Liu, CoNLL 2026)

ACL

• Ying Xia and Wei Liu. 2026. Event-Guided Slot Interaction for Multi-Domain Dialogue State Tracking. In Proceedings of the 30th Conference on Computational Natural Language Learning, pages 515–525, San Diego, California, USA. Association for Computational Linguistics.