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.

翻译：文档级事件抽取是一个长期存在的具有挑战性的信息检索问题，涉及一系列子任务：实体抽取、事件类型判断以及针对特定事件类型的多事件抽取。然而，将这一问题作为多个学习任务来处理会导致模型复杂度增加。此外，现有方法未能充分利用跨不同事件的实体之间的关联性，导致事件抽取性能受限。本文提出了一种新颖的文档级事件抽取框架，该框架引入了一种称为Token-事件-角色的新型数据结构以及一个多通道论元角色预测模块。所提出的数据结构使我们的模型能够揭示Token在多个事件中的主要角色，从而促进对事件关系更全面的理解。通过利用多通道预测模块，我们将实体和多事件抽取转化为单个预测Token-事件对的任务，从而减少了总体参数量并提升了模型效率。实验结果表明，我们的方法在F1分数上比现有最先进方法高出9.5个百分点，彰显了其在事件抽取中的优越性能。此外，消融研究证实了所提出的数据结构在改进事件抽取任务中的显著价值，进一步验证了其在提升框架整体性能方面的重要性。