  With the advancement of multimedia technologies, news documents and
user-generated content are often represented as multiple modalities, making
Multimedia Event Extraction (MEE) an increasingly important challenge. However,
recent MEE methods employ weak alignment strategies and data augmentation with
simple classification models, which ignore the capabilities of natural
language-formulated event templates for the challenging Event Argument
Extraction (EAE) task. In this work, we focus on EAE and address this issue by
introducing a unified template filling model that connects the textual and
visual modalities via textual prompts. This approach enables the exploitation
of cross-ontology transfer and the incorporation of event-specific semantics.
Experiments on the M2E2 benchmark demonstrate the effectiveness of our
approach. Our system surpasses the current SOTA on textual EAE by +7% F1, and
performs generally better than the second-best systems for multimedia EAE.
