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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 across totally 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 author 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 tracking (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 creator Yan Yang creator 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 conference publication Dialogue
state tracker is liable for inferring user intentions by 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 long dialogue context tracking.

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