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« on: January 25, 2023, 06:04:06 PM »
To handle these phenomena, we propose 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-specific options from the original dialogue and then 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 creator 2020-nov text Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online conference 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 structure to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying Hu author Yan Yang author Chencai Chen author Liang He creator Zhou Yu author 2020-jul text 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 user intentions by means of dialogue historical past. We propose a Dialogue State Tracker with slot wallet [pr] Attention and Slot Information Sharing (SAS) to scale back redundant information’s interference and enhance long dialogue context tracking.