To handle these phenomena, we propose 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 original dialogue after which integrate them utilizing a slot info sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang author Yi Guo creator 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 area ontology and unavailability of some values are two inevitable problems of dialogue state monitoring (DST). In this paper, we propose a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. SAS: Dialogue State Tracking by way of Slot Attention and Slot Information Sharing Jiaying Hu creator Yan Yang creator 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 consumer intentions via dialogue history. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to cut back redundant information’s interference and enhance long dialogue context monitoring.