To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across totally different domains. Specially, we first apply a Slot Attention to learn a set of slot-specific options from the unique dialogue and then integrate them using a slot info sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo author 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 tracking (DST). On this paper, we suggest a 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 writer Yan Yang creator Chencai Chen author Liang He author Zhou Yu creator 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 responsible for inferring consumer intentions by means of dialogue history. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to cut back redundant information’s interference and improve lengthy dialogue context tracking.

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