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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 completely 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 writer Siqi Zhu author 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 area ontology and unavailability of some values
are two inevitable problems of dialogue state monitoring (DST).
In this paper, we suggest a new structure to cleverly exploit
ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN.
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing
Jiaying Hu writer Yan Yang writer Chencai Chen writer Liang He creator
Zhou Yu author 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 answerable for
inferring user intentions through dialogue history.
We propose a Dialogue State Tracker with Slot Attention and Slot
Information Sharing (SAS) to reduce redundant information’s interference and
improve lengthy dialogue context monitoring.