EADRE: Event-type Aware Dynamic Representation of Entities in Document-level Event Extraction
Guangjun Zhang, Zhang Hu, Ru Li, Hongye Tan · ACM Transactions on Asian and Low-Resource Language Information Processing · 2024
Document-level event extraction aims to identify event types and arguments from one document. However, existing methods fail to consider semantic distinctions between multiple mentions of one entity and ignore dynamic representation of entities across multiple events simultaneously. Therefore, the models cannot capture flexible and specific entity representations in different event types. In this article, we propose EADRE ( E vent-type- A ware D ynamic R epresentation of E ntities). Specifically, we use cross-attention between mentions and event-type prototypes to obtain event-type-aware mention features. Then, we propose ASGate ( A daptive S oft G ate), which adaptively selects mention features to reduce the influence of event-unrelated mentions. EADRE introduces no more than 1% new parameters compared with the base model and has good transportability. Experiments on two public datasets show that EADRE improves the performance of multi-event extraction by 2.6% and 3.1%, as well as outperforms previous state-of-the-art baselines by 0.2% and 1.6%, with lower resource consumption without the use of pre-trained models. Further experimental analysis shows that EADRE significantly improves extraction performance in O2M and M2M multi-event scenarios.