Retrieval-Augmented Document-Level Event Extraction with Cross-Attention Fusion

Document-level event extraction intends to extract event records from an entire document. Current approaches adopt an entity-centric workflow, wherein the effectiveness of event extraction heavily relies on the input representation. Nonetheless, the input representations derived from earlier approaches exhibit incongruities when applied to the task of event extraction. To mitigate these discrepancies, we propose a Retrieval-Augmented Document-level Event Extraction (RADEE) method that leverages instances from the training dataset as supplementary event-informed knowledge. Specifically, the most similar training instance containing event records is retrieved and then concatenated with the input to enhance the input representation. To effectively integrate information from retrieved instances while minimizing noise interference, we introduce a fusion layer based on cross-attention mechanism. Experimental results obtained from a comprehensive evaluation of a large-scale document-level event extraction dataset reveal that our proposed method surpasses the performance of all baseline models. Furthermore, our approach exhibits improved performance even in low-resource settings, emphasizing its effectiveness and adaptability.