@inproceedings{trinh-etal-2020-energy,
    title = "Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking",
    author = "Trinh, Anh Duong  and
      Ross, Robert J.  and
      Kelleher, John D.",
    editor = "Agrawal, Priyanka  and
      Kozareva, Zornitsa  and
      Kreutzer, Julia  and
      Lampouras, Gerasimos  and
      Martins, Andr{\'e}  and
      Ravi, Sujith  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2020.spnlp-1.5/",
    doi = "10.18653/v1/2020.spnlp-1.5",
    pages = "33--42",
    abstract = "Scaling up dialogue state tracking to multiple domains is challenging due to the growth in the number of variables being tracked. Furthermore, dialog state tracking models do not yet explicitly make use of relationships between dialogue variables, such as slots across domains. We propose using energy-based structure prediction methods for large-scale dialogue state tracking task in two multiple domain dialogue datasets. Our results indicate that: (i) modelling variable dependencies yields better results; and (ii) the structured prediction output aligns with the dialogue slot-value constraint principles. This leads to promising directions to improve state-of-the-art models by incorporating variable dependencies into their prediction process."
}