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To handle these phenomena, we suggest a Dialogue State Tracking with
Slot Connections (DST-SC) mannequin to explicitly consider slot correlations throughout different domains.
Specially, we first apply a Slot Attention to learn a set
of slot-particular options from the unique dialogue and then integrate
them utilizing a slot information sharing module. Slot Attention with Value
Normalization for Multi-Domain Dialogue State Tracking
Yexiang Wang creator Yi Guo writer Siqi Zhu writer 2020-nov text Proceedings of
the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online convention publication Incompleteness of domain 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), referred to as SAVN. SAS: Dialogue State Tracking by way
of Slot Attention and Slot Information Sharing Jiaying Hu creator Yan Yang writer Chencai Chen writer
Liang He author Zhou Yu author 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 chargeable for
inferring user intentions through dialogue history. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and improve lengthy dialogue context tracking.

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