To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations throughout completely different domains. Specially, we first apply a Slot Attention to learn a set of slot-particular options from the unique dialogue after which integrate them using a slot info sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang creator Yi Guo writer Siqi Zhu author 2020-nov text 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). On this paper, we suggest 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 through Slot Attention and Slot Information Sharing Jiaying Hu author Yan Yang writer Chencai Chen author Liang He writer Zhou Yu writer 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 accountable for inferring user intentions via dialogue history. We propose 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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