To handle these phenomena, we suggest 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 learn a set of
slot-specific features from the original dialogue and then integrate them utilizing a slot data sharing
module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi
Guo author Siqi Zhu creator 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
issues of dialogue state monitoring (DST). On this paper, we suggest a brand 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 creator
Chencai Chen writer Liang He author Zhou Yu creator 2020-jul textual content
Proceedings of the 58th Annual Meeting of the Association for Computational
Linguistics Association for Computational Linguistics Online convention publication Dialogue state tracker is answerable for inferring consumer
intentions by means of 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 lengthy dialogue context tracking.

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