@inproceedings{chen-etal-2020-neural,
    title = "Neural Dialogue State Tracking with Temporally Expressive Networks",
    author = "Chen, Junfan  and
      Zhang, Richong  and
      Mao, Yongyi  and
      Xu, Jie",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.findings-emnlp.142/",
    doi = "10.18653/v1/2020.findings-emnlp.142",
    pages = "1570--1579",
    abstract = "Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to improve the accuracy of turn-level-state prediction and the state aggregation."
}