@inproceedings{jia-etal-2021-doubt-improving,
title = "When in Doubt: Improving Classification Performance with Alternating Normalization",
author = "Jia, Menglin and
Reiter, Austin and
Lim, Ser-Nam and
Artzi, Yoav and
Cardie, Claire",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.148",
doi = "10.18653/v1/2021.findings-emnlp.148",
pages = "1716--1723",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T When in Doubt: Improving Classification Performance with Alternating Normalization
%A Jia, Menglin
%A Reiter, Austin
%A Lim, Ser-Nam
%A Artzi, Yoav
%A Cardie, Claire
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F jia-etal-2021-doubt-improving
%X 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.
%R 10.18653/v1/2021.findings-emnlp.148
%U https://aclanthology.org/2021.findings-emnlp.148
%U https://doi.org/10.18653/v1/2021.findings-emnlp.148
%P 1716-1723
Markdown (Informal)
[When in Doubt: Improving Classification Performance with Alternating Normalization](https://aclanthology.org/2021.findings-emnlp.148) (Jia et al., Findings 2021)
ACL