@inproceedings{williams-2019-neural,
    title = "Neural Lexicons for Slot Tagging in Spoken Language Understanding",
    author = "Williams, Kyle",
    editor = "Loukina, Anastassia  and
      Morales, Michelle  and
      Kumar, Rohit",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/N19-2011/",
    doi = "10.18653/v1/N19-2011",
    pages = "83--89",
    abstract = "We explore the use of lexicons or gazettes in neural models for slot tagging in spoken language understanding. We develop models that encode lexicon information as neural features for use in a Long-short term memory neural network. Experiments are performed on data from 4 domains from an intelligent assistant under conditions that often occur in an industry setting, where there may be: 1) large amounts of training data, 2) limited amounts of training data for new domains, and 3) cross domain training. Results show that the use of neural lexicon information leads to a significant improvement in slot tagging, with improvements in the F-score of up to 12{\%}. Our findings have implications for how lexicons can be used to improve the performance of neural slot tagging models."
}