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A key bottleneck in building automatic extraction models for visually richdocuments like invoices is the cost of acquiring the several thousandhigh-quality labeled documents that are needed to train a model with acceptableaccuracy. We propose Selective Labeling to simplify the labeling task toprovide "yes/no" labels for candidate extractions predicted by a model trainedon partially labeled documents. We combine this with a custom active learningstrategy to find the predictions that the model is most uncertain about. Weshow through experiments on document types drawn from 3 different domains thatselective labeling can reduce the cost of acquiring labeled data by $10\times$with a negligible loss in accuracy.

Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models