Abstract
Label noise—incorrectly or ambiguously labeled training examples—can negatively impact model performance. Although noise detection techniques have been around for decades, practitioners rarely apply them, as manual noise remediation is a tedious process. Examples incorrectly flagged as noise waste reviewers’ time, and correcting label noise without guidance can be difficult. We propose LNIC, a noise-detection method that uses an example’s neighborhood within the training set to (a) reduce false positives and (b) provide an explanation as to why the ex- ample was flagged as noise. We demonstrate on several short-text classification datasets that LNIC outperforms the state of the art on measures of precision and F0.5-score. We also show how LNIC’s training set context helps a reviewer to understand and correct label noise in a dataset. The LNIC tool lowers the barriers to label noise remediation, increasing its utility for NLP practitioners.- Anthology ID:
- 2020.acl-demos.21
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Asli Celikyilmaz, Tsung-Hsien Wen
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 157–186
- Language:
- URL:
- https://aclanthology.org/2020.acl-demos.21
- DOI:
- 10.18653/v1/2020.acl-demos.21
- Cite (ACL):
- Michael Desmond, Catherine Finegan-Dollak, Jeff Boston, and Matt Arnold. 2020. Label Noise in Context. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 157–186, Online. Association for Computational Linguistics.
- Cite (Informal):
- Label Noise in Context (Desmond et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.acl-demos.21.pdf