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To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC) mannequin to explicitly consider slot correlations throughout completely different domains.
Specially, we first apply a Slot Attention to study a set of slot-specific
features from the unique dialogue after which integrate them using a
slot information sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang author Yi Guo author Siqi Zhu author 2020-nov textual content Proceedings of
the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics Online convention publication Incompleteness of area ontology and unavailability of some values
are two inevitable issues of dialogue state monitoring (DST).
On this paper, we propose a new structure 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
creator Chencai Chen author Liang He creator 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 responsible for inferring person intentions by dialogue historical past.
We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce
redundant information’s interference and enhance long dialogue context tracking.
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