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To handle these phenomena, we propose a Dialogue State Tracking
with Slot Connections (DST-SC) mannequin to explicitly consider slot correlations throughout totally different domains.

Specially, we first apply a Slot Attention to study a set of slot-particular features from the unique dialogue after which integrate them using a slot data sharing module.
Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo
writer Siqi Zhu creator 2020-nov textual content Proceedings
of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online convention publication Incompleteness of area ontology and unavailability of
some values are two inevitable problems of dialogue state
tracking (DST). On this paper, we suggest a 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 writer 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 liable for inferring consumer intentions
by way of dialogue historical past. We propose a Dialogue
State Tracker with Slot Attention and Slot Information Sharing (SAS) to scale back redundant information’s interference and improve lengthy dialogue context monitoring.

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