PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912387945201664 |
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| author | Zent, Matthew Smith, Digory Woodhead, Simon |
| author_facet | Zent, Matthew Smith, Digory Woodhead, Simon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16931 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues Zent, Matthew Smith, Digory Woodhead, Simon Computation and Language 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. |
| title | PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.16931 |