The Re-Label Method For Data-Centric Machine Learning
This addresses data quality issues for practitioners in industry deep learning, but it is incremental as it builds on existing data-centric approaches.
The paper tackles the problem of noisy manually labeled data in industry deep learning applications by introducing a method to identify and re-label noisy data using model predictions as references, achieving over 90 score in dev dataset evaluations.
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.