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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 across completely different domains.
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combine them using a slot data sharing module. Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer 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 convention publication Incompleteness of area 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 through Slot Attention and Slot Information Sharing Jiaying Hu author Yan Yang creator Chencai Chen creator
Liang He writer Zhou Yu creator 2020-jul textual content 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 consumer intentions by means of dialogue historical past.
We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to scale back redundant information’s
interference and enhance lengthy dialogue context monitoring.
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