GEMS: Generation-Based Event Argument Extraction via Multi-perspective Prompts and Ontology Steering

Generative methods significantly advance event argument extraction by probabilistically generating event argument sequences in a structured format.However, existing approaches primarily rely on a single prompt to generate event arguments in a fixed, predetermined order.Such a rigid approach overlooks the complex structural and dynamic interdependencies among event arguments.In this work, we present GEMS, a multi-prompt learning framework that Generates Event arguments via Multi-perspective prompts and ontology Steering.Specifically, GEMS utilizes multiple unfilled prompts for each sentence, predicting event arguments in varying sequences to explicitly capture the interrelationships between arguments.These predictions are subsequently aggregated using a voting mechanism.Furthermore, an ontologydriven steering mechanism is proposed to ensure that the generated arguments are contextually appropriate and consistent with eventspecific knowledge.Extensive experiments on two benchmark datasets demonstrate that GEMS achieves state-of-the-art performance, particularly in low-resource settings.