@inproceedings{f20dfaefbe0045b3bc10c8e382a522be,
title = "SAS: Dialogue state tracking via slot attention and slot information sharing",
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. The sharing improve the models ability to deduce value from related slots. Our model yields a significantly improved performance compared to previous state-of-the-art models on the MultiWOZ dataset.",
author = "Jiaying Hu and Yan Yang and Chengcai Chen and Liang He and Zhou Yu",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics; 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 ; Conference date: 05-07-2020 Through 10-07-2020",
year = "2020",
language = "英语",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "6366--6375",
booktitle = "ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
address = "澳大利亚",
}