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Marylin 在 2024/1/18 2:17:35 发表评论：
IP：184.82.8.141 内容：To handle these phenomena, we suggest a Dialogue State Tracking with Slot Connections (DST-SC)
model to explicitly consider slot correlations across different
domains. Specially, we first apply a Slot Attention to be taught a set
of slot-particular features from the original dialogue after which integrate 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 writer 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 suggest 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 writer Yan Yang creator Chencai Chen writer Liang He creator Zhou Yu writer 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 responsible for inferring person intentions via
dialogue historical past. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information’s interference and enhance lengthy dialogue
context monitoring.
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