@inproceedings{zent-etal-2025-piivot,
    title = "{PII}vot: A Lightweight {NLP} Anonymization Framework for Question-Anchored Tutoring Dialogues",
    author = "Zent, Matthew  and
      Smith, Digory  and
      Woodhead, Simon",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1397/",
    pages = "27467--27476",
    ISBN = "979-8-89176-332-6",
    abstract = "Personally identifiable information (PII) anonymization is a high-stakes task that poses a barrier to many open-science data sharing initiatives. While PII identification has made large strides in recent years, in practice, error thresholds and the recall/precision trade-off still limit the uptake of these anonymization pipelines. We present PIIvot, a lighter-weight framework for PII anonymization that leverages knowledge of the data context to simplify the PII detection problem. To demonstrate its effectiveness, we also contribute QATD{\_}2k, the largest open-source real-world tutoring dataset of its kind, to support the demand for quality educational dialogue data."
}