@inproceedings{oi-miwa-2025-enhancing,
    title = "Enhancing {NER} by Harnessing Multiple Datasets with Conditional Variational Autoencoders",
    author = "Oi, Taku  and
      Miwa, Makoto",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.acl-short.87/",
    doi = "10.18653/v1/2025.acl-short.87",
    pages = "1107--1117",
    ISBN = "979-8-89176-252-7",
    abstract = "We propose a novel method to integrate a Conditional Variational Autoencoder (CVAE) into a span-based Named Entity Recognition (NER) model to model the shared and unshared information among labels in multiple datasets and ease the training on the datasets. Experimental results using multiple biomedical datasets show the effectiveness of the proposed method, achieving improved performance on the BioRED dataset."
}