This paper presents a novel approach of leveraging Inter-Annotator Agreement (IAA), traditionally used for assessing labeling consistency, to optimize Data Management Operations (DMOps). We advocate for the use of IAA in predicting the labeling quality of individual annotators, leading to cost and time efficiency in data production. Additionally, our work highlights the potential of IAA in forecasting document difficulty, thereby boosting the data construction process's overall efficiency. This research underscores IAA's broader application potential in data-driven research optimization and holds significant implications for large-scale data projects prioritizing efficiency, cost reduction, and high-quality data.
@article{arxiv.2306.14374,
title = {Transcending Traditional Boundaries: Leveraging Inter-Annotator Agreement (IAA) for Enhancing Data Management Operations (DMOps)},
author = {Damrin Kim and NamHyeok Kim and Chanjun Park and Harksoo Kim},
journal= {arXiv preprint arXiv:2306.14374},
year = {2023}
}
Comments
Accepted for Data-centric Machine Learning Research (DMLR) Workshop at ICML 2023