To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) 
model to explicitly consider slot correlations across completely different domains.

Specially, we first apply a Slot Attention to be taught a 
set of slot-particular options from the unique dialogue 
and then combine them utilizing a slot data sharing module.
Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang author Yi 
Guo author Siqi Zhu creator 2020-nov textual content Proceedings of the 2020 
Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online conference publication Incompleteness of area 
ontology and unavailability of some values are two inevitable issues of dialogue 
state tracking (DST). In this paper, we suggest 
a new structure to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), known as SAVN.

SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying 
Hu creator Yan Yang writer Chencai Chen writer Liang 
He author Zhou Yu writer 2020-jul textual content Proceedings of 
the 58th Annual Meeting of the Association for Computational Linguistics 
Association for Computational Linguistics Online conference publication Dialogue state tracker is chargeable for inferring user intentions via dialogue historical past.

We propose a Dialogue State Tracker with Slot Attention and Slot 
Information Sharing (SAS) to cut back redundant information’s interference 
and improve long dialogue context tracking.

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