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        <title>PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues
%A Zent, Matthew
%A Smith, Digory
%A Woodhead, Simon
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zent-etal-2025-piivot
%X 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.
%R 10.18653/v1/2025.emnlp-main.1397
%U https://aclanthology.org/2025.emnlp-main.1397/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1397
%P 27479-27488

Markdown (Informal)

[PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues](https://aclanthology.org/2025.emnlp-main.1397/) (Zent et al., EMNLP 2025)

• PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues (Zent et al., EMNLP 2025)

ACL

• Matthew Zent, Digory Smith, and Simon Woodhead. 2025. PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 27479–27488, Suzhou, China. Association for Computational Linguistics.