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To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations throughout totally different domains. Specially, we first apply a Slot Attention to learn a set of slot-specific options 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 creator Siqi Zhu author 2020-nov text Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online conference publication Incompleteness of domain ontology and unavailability of some values are two inevitable issues 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), known as SAVN. SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang creator Chencai Chen creator Liang He writer Zhou Yu creator 2020-jul textual content Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics Online convention publication Dialogue state tracker is liable for inferring user intentions by 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 tracking.

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