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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 different domains.
  Specially, we first apply a Slot Attention to learn a set
  of slot-particular options 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 creator Yi
  Guo author Siqi Zhu author 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 domain ontology and unavailability of some values are two inevitable issues
  of dialogue state monitoring (DST). On this paper, we propose a new architecture to cleverly exploit ontology,
  which consists of Slot Attention (SA) and Value Normalization (VN), known as SAVN.
  
  SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing Jiaying Hu writer Yan Yang writer Chencai Chen author 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 convention publication Dialogue state tracker is accountable for inferring person intentions by way of dialogue history.
  We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to
  cut back redundant information’s interference and improve long dialogue context tracking.
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