Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction

Abstract

Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis. Prior classification-based models still fail to explicitly capture significant relationships and heavily relies on large-scale datasets. In this study, we propose a novel approach called Generation-Augmented and Embedding Fusion. This approach first uses predefined templates and generative language models to produce an embedding capturing role relationship information, then integrates it into the foundational embedding derived from a classification model through a noval embedding fusion mechanism. We conduct the extensive experiments on the RAMS and WikiEvents datasets to demonstrate that our approach is more effective than the baselines, and that it is also data-efficient in low-resource scenarios.