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To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC)
model to explicitly consider slot correlations throughout totally different domains.
Specially, we first apply a Slot Attention to study a set of slot-particular options from
the unique dialogue and then integrate them using a slot information sharing module.
Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang
creator Yi Guo creator 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 convention publication Incompleteness of area ontology and
unavailability of some values are two inevitable problems
of dialogue state tracking (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 by way of Slot Attention and
Slot Information Sharing Jiaying Hu creator Yan Yang creator
Chencai Chen author Liang He author Zhou Yu author 2020-jul text Proceedings of the 58th Annual
Meeting of the Association for Computational Linguistics Association for
Computational Linguistics Online convention publication Dialogue
state tracker is responsible for inferring person intentions via 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 monitoring.