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To handle these phenomena, we suggest 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 be taught a set of slot-specific options from the original dialogue after which integrate them
using a slot information sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo creator Siqi
Zhu writer 2020-nov textual content 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 monitoring (DST).

In this paper, we propose a brand 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
creator Yan Yang writer Chencai Chen creator Liang He creator Zhou Yu writer 2020-jul
textual content Proceedings of the 58th Annual Meeting of
the Association for Computational Linguistics Association for Computational Linguistics Online conference publication Dialogue state tracker is answerable for inferring user
intentions through 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 enhance long dialogue context monitoring.