The Re-Label Method For Data-Centric Machine Learning

9 Feb 2023  ·  Tong Guo ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Label Error Detection TREC-6 Accuracy 99.0 # 1
Text Classification TREC-6 Automatic Label Error Correction Error 1.00 # 1


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