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Document-level event extraction is a long-standing challenging informationretrieval problem involving a sequence of sub-tasks: entity extraction, eventtype judgment, and event type-specific multi-event extraction. However,addressing the problem as multiple learning tasks leads to increased modelcomplexity. Also, existing methods insufficiently utilize the correlation ofentities crossing different events, resulting in limited event extractionperformance. This paper introduces a novel framework for document-level eventextraction, incorporating a new data structure called token-event-role and amulti-channel argument role prediction module. The proposed data structureenables our model to uncover the primary role of tokens in multiple events,facilitating a more comprehensive understanding of event relationships. Byleveraging the multi-channel prediction module, we transform entity andmulti-event extraction into a single task of predicting token-event pairs,thereby reducing the overall parameter size and enhancing model efficiency. Theresults demonstrate that our approach outperforms the state-of-the-art methodby 9.5 percentage points in terms of the F1 score, highlighting its superiorperformance in event extraction. Furthermore, an ablation study confirms thesignificant value of the proposed data structure in improving event extractiontasks, further validating its importance in enhancing the overall performanceof the framework.

Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction