Abstract
Certainty calibration is an important goal on the path to interpretability and trustworthy AI. Particularly in the context of human-in-the-loop systems, high-quality low to mid-range certainty estimates are essential. In the presence of a dominant high-certainty class, for instance the non-entity class in NER problems, existing calibration error measures are completely insensitive to potentially large errors in this certainty region of interest. We introduce a region-balanced calibration error metric that weights all certainty regions equally. When low and mid certainty estimates are taken into account, calibration error is typically larger than previously reported. We introduce a simple extension of temperature scaling, requiring no additional computation, that can reduce both traditional and region-balanced notions of calibration error over existing baselines.- Anthology ID:
- 2022.acl-short.59
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 538–544
- Language:
- URL:
- https://aclanthology.org/2022.acl-short.59
- DOI:
- 10.18653/v1/2022.acl-short.59
- Cite (ACL):
- Hillary Dawkins and Isar Nejadgholi. 2022. Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 538–544, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification (Dawkins & Nejadgholi, ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.acl-short.59.pdf
- Data
- Few-NERD