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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 throughout totally different domains. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue after which combine them utilizing a slot info sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo creator Siqi Zhu creator 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 issues of dialogue state monitoring (DST). In this paper, we suggest 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 by way of Slot Attention and Slot Information Sharing Jiaying Hu creator Yan Yang writer Chencai Chen author Liang He creator Zhou Yu creator 2020-jul text 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 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 enhance long dialogue context monitoring.
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