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To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections
(DST-SC) mannequin to explicitly consider slot correlations across totally
different domains. Specially, we first apply a Slot Attention to learn a set of slot-specific options from the unique
dialogue and then combine them utilizing a slot information sharing module.
Slot Attention with Value Normalization for Multi-Domain Dialogue
State Tracking Yexiang Wang writer Yi Guo author Siqi Zhu writer 2020-nov text Proceedings of the 2020 Conference on Empirical Methods in Natural
Language Processing (EMNLP) Association for Computational Linguistics Online conference publication Incompleteness of domain ontology and unavailability
of some values are two inevitable issues of dialogue state tracking (DST).

On this paper, we propose a new architecture to cleverly exploit ontology,
which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN.
SAS: Dialogue State Tracking through Slot Attention and Slot Information Sharing Jiaying Hu writer
Yan Yang writer Chencai Chen creator Liang He writer Zhou Yu author 2020-jul text
Proceedings of the 58th Annual Meeting of the Association for Computational
Linguistics Association for Computational Linguistics
Online convention publication Dialogue state tracker is accountable
for inferring user intentions by way of dialogue historical past.
We suggest 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.