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The Re-Label Method For Data-Centric Machine Learning

Machine Learning 2025-03-20 v9 Computation and Language

Abstract

In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The dev dataset evaluation results and human evaluation results verify our idea.

Keywords

Cite

@article{arxiv.2302.04391,
  title  = {The Re-Label Method For Data-Centric Machine Learning},
  author = {Tong Guo},
  journal= {arXiv preprint arXiv:2302.04391},
  year   = {2025}
}
R2 v1 2026-06-28T08:35:32.377Z