@inproceedings{hu-etal-2020-sas,
    title = "{SAS}: Dialogue State Tracking via Slot Attention and Slot Information Sharing",
    author = "Hu, Jiaying  and
      Yang, Yan  and
      Chen, Chencai  and
      He, Liang  and
      Yu, Zhou",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.acl-main.567/",
    doi = "10.18653/v1/2020.acl-main.567",
    pages = "6366--6375",
    abstract = "Dialogue state tracker is responsible for inferring user intentions through dialogue history. Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information{'}s interference and improve long dialogue context tracking. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue and then integrate them using a slot information sharing module. Our model yields a significantly improved performance compared to previous state-of the-art models on the MultiWOZ dataset."
}