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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 throughout completely
different domains. Specially, we first apply a Slot Attention to be taught a set of slot-specific features from the unique dialogue and then combine them utilizing a slot info sharing module.
Slot Attention with Value Normalization for Multi-Domain Dialogue
State Tracking Yexiang Wang author 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).
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 through Slot Attention and Slot Information Sharing Jiaying Hu author Yan Yang author Chencai Chen creator Liang He author Zhou Yu creator 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 accountable for inferring person intentions
through dialogue history. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and improve long
dialogue context tracking.
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