To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across completely different domains. Specially, we first apply a Slot Attention to learn a set of slot-particular options from the original dialogue after which combine them utilizing a slot information sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang author Yi Guo author 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 conference publication Incompleteness of area ontology and unavailability of some values are two inevitable problems of dialogue state monitoring (DST). On 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 through Slot Attention and Slot Information Sharing Jiaying Hu creator Yan Yang creator Chencai Chen writer Liang He author Zhou Yu author 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 answerable for inferring person intentions via dialogue historical past. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and enhance lengthy dialogue context monitoring.
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