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  To handle these phenomena, we propose a Dialogue
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  domains. Specially, we first apply a Slot Attention to be taught a set of slot-specific options from the unique dialogue after which integrate them utilizing a slot information sharing module.
  Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking
  Yexiang Wang creator Yi Guo writer 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 domain ontology and unavailability of some values
  are two inevitable issues of dialogue state monitoring (DST).
  
  On this paper, we suggest 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
  creator Yan Yang writer Chencai Chen writer 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 chargeable for inferring user
  intentions by way of dialogue historical past. We suggest a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to cut back
  redundant information’s interference and improve lengthy dialogue context monitoring.
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