When in Doubt: Improving Classification Performance with Alternating Normalization
Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoav Artzi, Claire Cardie
Abstract
We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution using the predicted class distributions of high-confidence validation examples. CAN is easily applicable to any probabilistic classifier, with minimal computation overhead. We analyze the properties of CAN using simulated experiments, and empirically demonstrate its effectiveness across a diverse set of classification tasks.- Anthology ID:
- 2021.findings-emnlp.148
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1716–1723
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.148
- DOI:
- 10.18653/v1/2021.findings-emnlp.148
- Cite (ACL):
- Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoav Artzi, and Claire Cardie. 2021. When in Doubt: Improving Classification Performance with Alternating Normalization. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1716–1723, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- When in Doubt: Improving Classification Performance with Alternating Normalization (Jia et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.findings-emnlp.148.pdf
- Code
- KMnP/can
- Data
- DialogRE