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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-particular options from the original dialogue and then combine them using a slot information sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo creator Siqi Zhu writer 2020-nov textual content Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online conference publication Incompleteness of area ontology and unavailability of some values are two inevitable problems of dialogue state monitoring (DST). On 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 through Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang writer Chencai Chen writer Liang He writer Zhou Yu author 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 chargeable for inferring consumer intentions by way of dialogue history. 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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