@inproceedings{gaspers-etal-2021-impact,
    title = "The impact of domain-specific representations on {BERT}-based multi-domain spoken language understanding",
    author = {Gaspers, Judith  and
      Do, Quynh  and
      R{\"o}ding, Tobias  and
      Bradford, Melanie},
    editor = "Ben-David, Eyal  and
      Cohen, Shay  and
      McDonald, Ryan  and
      Plank, Barbara  and
      Reichart, Roi  and
      Rotman, Guy  and
      Ziser, Yftah",
    booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
    month = apr,
    year = "2021",
    address = "Kyiv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.adaptnlp-1.4/",
    pages = "28--32",
    abstract = "This paper provides the first experimental study on the impact of using domain-specific representations on a BERT-based multi-task spoken language understanding (SLU) model for multi-domain applications. Our results on a real-world dataset covering three languages indicate that by using domain-specific representations learned adversarially, model performance can be improved across all of the three SLU subtasks domain classification, intent classification and slot filling. Gains are particularly large for domains with limited training data."
}