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To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across different domains. Specially, we first apply a Slot Attention to study a set of slot-specific features from the original dialogue after which combine them using a slot data 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 area ontology and unavailability of some values are two inevitable problems of dialogue state monitoring (DST). In this paper, we suggest a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), known as SAVN. SAS: Dialogue State Tracking through Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang writer Chencai Chen creator Liang He creator Zhou Yu creator 2020-jul text Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online conference publication Dialogue state tracker is accountable for inferring person intentions through 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 monitoring.