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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 totally different domains. Specially, we first apply a Slot Attention to study a set of slot-specific options from the original dialogue after which integrate them utilizing a slot information sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo author 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 convention publication Incompleteness of domain ontology and unavailability of some values are two inevitable problems of dialogue state monitoring (DST). In this paper, we propose a new structure 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 creator Chencai Chen writer Liang He writer Zhou Yu writer 2020-jul text Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online convention publication Dialogue state tracker is answerable for inferring user intentions by means of dialogue history. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to cut back redundant information’s interference and improve lengthy dialogue context monitoring.
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