DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction Model with Slot Querying

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.