To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) mannequin to explicitly consider slot correlations
across different domains. Specially, we first apply a Slot Attention to learn a set of slot-particular options from the
original dialogue after which combine them using a
slot info sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang
author 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
convention publication Incompleteness of domain ontology and unavailability of some values
are two inevitable problems of dialogue state monitoring (DST).
On this paper, we propose a brand new structure to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN.
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang author Chencai Chen author Liang He author Zhou Yu writer 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 answerable for inferring person intentions
by means of dialogue historical past. We propose a Dialogue State
Tracker with Slot Attention and Slot Information Sharing (SAS) to
reduce redundant information’s interference and enhance long dialogue context monitoring.

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