@inproceedings{trinh-etal-2019-energy,
    title = "Energy-Based Modelling for Dialogue State Tracking",
    author = "Trinh, Anh Duong  and
      Ross, Robert  and
      Kelleher, John",
    editor = "Chen, Yun-Nung  and
      Bedrax-Weiss, Tania  and
      Hakkani-Tur, Dilek  and
      Kumar, Anuj  and
      Lewis, Mike  and
      Luong, Thang-Minh  and
      Su, Pei-Hao  and
      Wen, Tsung-Hsien",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/W19-4109/",
    doi = "10.18653/v1/W19-4109",
    pages = "77--86",
    abstract = "The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including input features and output labels. We demonstrate that the energy-based approach improves the performance of a deep learning dialogue state tracker towards state-of-the-art results without the need for many of the other steps required by current state-of-the-art methods."
}