Celia's Diary

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To handle these phenomena, we propose 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 learn a set of slot-specific options
from the original dialogue after which integrate them using a slot information sharing module.

Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking Yexiang Wang writer Yi Guo author 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 propose 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 writer Yan Yang author Chencai Chen creator Liang He writer Zhou Yu creator 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 liable for inferring person intentions by way
of dialogue history. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant
information’s interference and improve lengthy
dialogue context tracking.

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