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  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.
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