Recent advancements in event argument extraction (EAE) involve incorporating
beneficial auxiliary information into models during training and inference,
such as retrieved instances and event templates. Additionally, some studies
introduce learnable prefix vectors to models. These methods face three
challenges: (1) insufficient utilization of relevant event instances due to
deficiencies in retrieval; (2) neglect of important information provided by
relevant event templates; (3) the advantages of prefixes are constrained due to
their inability to meet the specific informational needs of EAE. In this work,
we propose DEGAP, which addresses the above challenges through two simple yet
effective components: (1) dual prefixes, where the instance-oriented prefix and
template-oriented prefix are trained to learn information from different event
instances and templates, respectively, and then provide relevant information as
cues to EAE model without retrieval; (2) event-guided adaptive gating
mechanism, which guides the prefixes based on the target event to fully
leverage their advantages. Extensive experiments demonstrate that our method
achieves new state-of-the-art performance on four datasets (ACE05, RAMS,
WIKIEVENTS, and MLEE). Further analysis verifies the importance of the proposed
design and the effectiveness of the main components.

DEGAP 提出了解决事件论元提取中的挑战的方法，通过引入对不同事件实例和模板学习信息的双重前缀以及基于目标事件引导前缀的自适应门控机制，该方法在四个数据集上达到了最新的最优性能。