@inproceedings{gangadharaiah-narayanaswamy-2019-joint,
    title = "Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog",
    author = "Gangadharaiah, Rashmi  and
      Narayanaswamy, Balakrishnan",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N19-1055/",
    doi = "10.18653/v1/N19-1055",
    pages = "564--569",
    abstract = "Neural network models have recently gained traction for sentence-level intent classification and token-based slot-label identification. In many real-world scenarios, users have multiple intents in the same utterance, and a token-level slot label can belong to more than one intent. We investigate an attention-based neural network model that performs multi-label classification for identifying multiple intents and produces labels for both intents and slot-labels at the token-level. We show state-of-the-art performance for both intent detection and slot-label identification by comparing against strong, recently proposed models. Our model provides a small but statistically significant improvement of 0.2{\%} on the predominantly single-intent ATIS public data set, and 55{\%} intent accuracy improvement on an internal multi-intent dataset."
}