Document-level event extraction is a long-standing challenging information
retrieval problem involving a sequence of sub-tasks: entity extraction, event
type judgment, and event type-specific multi-event extraction. However,
addressing the problem as multiple learning tasks leads to increased model
complexity. Also, existing methods insufficiently utilize the correlation of
entities crossing different events, resulting in limited event extraction
performance. This paper introduces a novel framework for document-level event
extraction, incorporating a new data structure called token-event-role and a
multi-channel argument role prediction module. The proposed data structure
enables our model to uncover the primary role of tokens in multiple events,
facilitating a more comprehensive understanding of event relationships. By
leveraging the multi-channel prediction module, we transform entity and
multi-event extraction into a single task of predicting token-event pairs,
thereby reducing the overall parameter size and enhancing model efficiency. The
results demonstrate that our approach outperforms the state-of-the-art method
by 9.5 percentage points in terms of the F1 score, highlighting its superior
performance in event extraction. Furthermore, an ablation study confirms the
significant value of the proposed data structure in improving event extraction
tasks, further validating its importance in enhancing the overall performance
of the framework.

本文提出了一种新的基于令牌 - 事件 - 角色数据结构和多通道预测模块的文档级事件抽取框架，并证明了其在各项性能指标表现优异。