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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 across totally different domains.
Specially, we first apply a Slot Attention to be taught a set of slot-particular features from the unique dialogue
after which combine them using a slot info sharing module.

Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking
Yexiang Wang creator Yi Guo creator Siqi Zhu creator 2020-nov text 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 problems of dialogue state tracking (DST).

In this paper, we suggest a brand new architecture to cleverly exploit ontology,
which consists of Slot Attention (SA) and Value Normalization (VN), known as SAVN.
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying Hu creator Yan Yang writer Chencai Chen author Liang He creator Zhou Yu creator 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 chargeable for inferring user
intentions by means 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 enhance long dialogue context monitoring.

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