@inproceedings{suresh-etal-2023-intermediate,
    title = "Intermediate Domain Finetuning for Weakly Supervised Domain-adaptive Clinical {NER}",
    author = "Suresh, Shilpa  and
      Tavabi, Nazgol  and
      Golchin, Shahriar  and
      Gilreath, Leah  and
      Garcia-Andujar, Rafael  and
      Kim, Alexander  and
      Murray, Joseph  and
      Bacevich, Blake  and
      Kiapour, Ata",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.29",
    doi = "10.18653/v1/2023.bionlp-1.29",
    pages = "320--325",
    abstract = "Accurate human-annotated data for real-worlduse cases can be scarce and expensive to obtain. In the clinical domain, obtaining such data is evenmore difficult due to privacy concerns which notonly restrict open access to quality data but also require that the annotation be done by domain experts. In this paper, we propose a novel framework - InterDAPT - that leverages Intermediate Domain Finetuning to allow language models to adapt to narrow domains with small, noisy datasets. By making use of peripherally-related, unlabeled datasets,this framework circumvents domain-specific datascarcity issues. Our results show that this weaklysupervised framework provides performance improvements in downstream clinical named entityrecognition tasks.",
}

Accurate human-annotated data for real-worlduse cases can be scarce and expensive to obtain. In the clinical domain, obtaining such data is evenmore difficult due to privacy concerns which notonly restrict open access to quality data but also require that the annotation be done by domain experts. In this paper, we propose a novel framework - InterDAPT - that leverages Intermediate Domain Finetuning to allow language models to adapt to narrow domains with small, noisy datasets. By making use of peripherally-related, unlabeled datasets,this framework circumvents domain-specific datascarcity issues. Our results show that this weaklysupervised framework provides performance improvements in downstream clinical named entityrecognition tasks.
Suresh, Shilpa Tavabi, Nazgol Golchin, Shahriar Gilreath, Leah Garcia-Andujar, Rafael Kim, Alex er Murray, Joseph Bacevich, Blake Kiapour, Ata
Intermediate Domain Finetuning for Weakly Supervised Domain-adaptive Clinical NER