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To handle these phenomena, we propose 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 be taught a set of slot-specific options from the unique dialogue after which combine them utilizing a slot information sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo writer Siqi Zhu writer 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 problems of dialogue state tracking (DST). In this paper, we propose a brand new architecture 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 author Liang He creator Zhou Yu author 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 chargeable for inferring consumer intentions by dialogue historical past. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to scale back redundant information’s interference and improve lengthy dialogue context tracking.
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