Schema-based Data Augmentation for Event Extraction

Abstract

Event extraction is a crucial task for semantic understanding and structured knowledge construction. However, the expense of collecting and labeling data for training event extraction models is usually high. To address this issue, we propose a novel schema-based data augmentation method that utilizes event schemas to guide the data generation process. The event schemas depict the typical patterns of complex events and can be used to create new synthetic data for event extraction. Specifically, we sub-sample from the schema graph to obtain a subgraph, instantiate the schema subgraph, and then convert the instantiated subgraph to natural language texts. We conduct extensive experiments on event trigger detection, event trigger extraction, and event argument extraction tasks using two datasets (including five scenarios). The experimental results demonstrate that our proposed data-augmentation method produces high-quality generated data and significantly enhances the model performance, with up to 12% increase in F1 score compared to baseline methods.

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