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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 throughout totally different domains. Specially, we first apply a Slot Attention to be taught a set of slot-specific features 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 author Yi Guo creator Siqi Zhu creator 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 tracking (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 by way of Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang author 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 person intentions via dialogue history. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and enhance lengthy dialogue context tracking.
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