Recent advancements in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference, such as retrieved instances and event templates. These methods face two challenges: (1) the retrieval results may be irrelevant and (2) templates are developed independently for each event without considering their possible relationship. In this work, we propose DEGAP to address these challenges through a simple yet effective components: dual prefixes, i.e. learnable prompt vectors, where the instance-oriented prefix and template-oriented prefix are trained to learn information from different event instances and templates. Additionally, we propose an event-guided adaptive gating mechanism, which can adaptively leverage possible connections between different events and thus capture relevant information from the prefix. Finally, these event-guided prefixes provide relevant information as cues to EAE model without retrieval. Extensive experiments demonstrate that our method achieves new state-of-the-art performance on four datasets (ACE05, RAMS, WIKIEVENTS, and MLEE). Further analysis shows the impact of different components.

翻译：近年来，事件论元提取（EAE）的研究进展涉及在训练和推理过程中将有用的辅助信息（如检索到的实例和事件模板）融入模型。这些方法面临两个挑战：（1）检索结果可能不相关；（2）模板是为每个事件独立开发的，未考虑它们之间可能的关系。在本工作中，我们提出DEGAP，通过一个简单而有效的组件——双重前缀（即可学习的提示向量）来解决这些挑战，其中实例导向的前缀和模板导向的前缀被训练以从不同的事件实例和模板中学习信息。此外，我们提出了一种事件引导的自适应门控机制，该机制能够自适应地利用不同事件之间的可能联系，从而从前缀中捕获相关信息。最后，这些事件引导的前缀在不进行检索的情况下，为EAE模型提供相关信息作为线索。大量实验表明，我们的方法在四个数据集（ACE05、RAMS、WIKIEVENTS和MLEE）上取得了新的最先进性能。进一步的分析展示了不同组件的影响。