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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 be taught
a set of slot-specific options from the original dialogue after which combine them utilizing
a slot information sharing module. Slot Attention with
Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang creator Yi
Guo author 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 conference publication Incompleteness of area ontology and unavailability of some values are
two inevitable issues of dialogue state monitoring (DST). In this paper, we propose a brand new architecture 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 creator Yan Yang
creator Chencai Chen writer 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 dialogue historical past. We propose a Dialogue State Tracker with
Slot Attention and Slot Information Sharing (SAS) to scale back redundant information’s interference and improve long dialogue context tracking.
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