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Data augmentation uses artificially-created examples to support supervisedmachine learning, adding robustness to the resulting models and helping toaccount for limited availability of labelled data. We apply and evaluate asynthetic data approach to relationship classification in digital libraries,generating artificial books with relationships that are common in digitallibraries but not easier inferred from existing metadata. We find that forclassification on whole-part relationships between books, synthetic dataimproves a deep neural network classifier by 91%. Further, we consider theability of synthetic data to learn a useful new text relationship class fromfully artificial training data.

Improving Text Relationship Modeling with Artificial Data