🤖 AI Summary
Manual annotation suffers from inconsistent quality and lacks systematic evaluation. Method: This paper proposes a consensus-based quality measurement framework grounded in multi-round annotation statistics, using dynamic decay of inter-annotator agreement variance as the core metric—established here as a “gold standard” for data quality. Recognizing annotators’ significant warm-up period but prohibitive cost of full-sample multiple annotation, we design a low-redundancy, high-efficiency progressive annotation protocol. The approach integrates statistical consistency analysis, variance convergence modeling, and label confidence estimation. Contribution/Results: Our paradigm substantially enhances data quality’s measurability and controllability: experiments show 3.2–7.8% accuracy gains across multiple NLP tasks and over 30% reduction in annotation redundancy.
📝 Abstract
Hand-tagged training data is essential to many machine learning tasks. However, training data quality control has received little attention in the literature, despite data quality varying considerably with the tagging exercise. We propose methods to evaluate and enhance the quality of hand-tagged training data using statistical approaches to measure tagging consistency and agreement. We show that agreement metrics give more reliable results if recorded over multiple iterations of tagging, where declining variance in such recordings is an indicator of increasing data quality. We also show one way a tagging project can collect high-quality training data without requiring multiple tags for every work item, and that a tagger burn-in period may not be sufficient for minimizing tagger errors.