@inproceedings{sasikumar-mantri-2023-transfer,
    title = "Transfer Learning for Low-Resource Clinical Named Entity Recognition",
    author = "Sasikumar, Nevasini  and
      Mantri, Krishna Sri Ipsit",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.clinicalnlp-1.53/",
    doi = "10.18653/v1/2023.clinicalnlp-1.53",
    pages = "514--518",
    abstract = "We propose a transfer learning method that adapts a high-resource English clinical NER model to low-resource languages and domains using only small amounts of in-domain annotated data. Our approach involves translating in-domain datasets to English, fine-tuning the English model on the translated data, and then transferring it to the target language/domain. Experiments on Spanish, French, and conversational clinical text datasets show accuracy gains over models trained on target data alone. Our method achieves state-of-the-art performance and can enable clinical NLP in more languages and modalities with limited resources."
}