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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 throughout different domains.
Specially, we first apply a Slot Attention to be taught a set of slot-specific options from the unique
dialogue after which combine them utilizing a slot data sharing module.
Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang author Yi Guo writer Siqi Zhu author
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 area ontology and unavailability of some values are two
inevitable problems of dialogue state monitoring (DST).
In this paper, we suggest 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 by way of Slot Attention and
Slot Information Sharing Jiaying Hu writer Yan Yang author Chencai Chen writer
Liang He writer Zhou Yu writer 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 liable for inferring consumer intentions by means
of dialogue historical past. We suggest a Dialogue
State Tracker with Slot Attention and Slot Information Sharing (SAS) to scale back redundant information’s interference and
enhance lengthy dialogue context tracking.