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To handle these phenomena, we suggest a Dialogue State Tracking
with Slot Connections (DST-SC) mannequin to
explicitly consider slot correlations across different domains.
Specially, we first apply a Slot Attention to learn a set of slot-specific features from the unique dialogue after which integrate
them utilizing a slot data 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 convention publication Incompleteness
of domain ontology and unavailability of some values are
two inevitable problems of dialogue state
tracking (DST). On 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 author Yan Yang author
Chencai Chen creator Liang He writer 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
person intentions by dialogue historical past. We propose a Dialogue State Tracker with Slot
Attention and Slot Information Sharing (SAS) to cut back redundant information’s interference and enhance lengthy dialogue context tracking. -
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