@inproceedings{zhong-etal-2018-global,
    title = "Global-Locally Self-Attentive Encoder for Dialogue State Tracking",
    author = "Zhong, Victor  and
      Xiong, Caiming  and
      Socher, Richard",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P18-1135/",
    doi = "10.18653/v1/P18-1135",
    pages = "1458--1467",
    abstract = "Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to shares parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states. GLAD obtains 88.3{\%} joint goal accuracy and 96.4{\%} request accuracy on the WoZ state tracking task, outperforming prior work by 3.9{\%} and 4.8{\%}. On the DSTC2 task, our model obtains 74.7{\%} joint goal accuracy and 97.3{\%} request accuracy, outperforming prior work by 1.3{\%} and 0.8{\%}"
}