Event argument extraction identifies arguments for predefined event roles in text. Traditional evaluations rely on exact match (EM), requiring predicted arguments to match annotated spans exactly. However, this approach fails for generative models like large language models (LLMs), which produce diverse yet semantically accurate responses. EM underestimates performance by disregarding valid variations, implicit arguments (unstated but inferable), and scattered arguments (distributed across a document). To bridge this gap, we introduce Reliable Evaluation framework for Generative event argument extraction (REGen), a framework that better aligns with human judgment. Across six datasets, REGen improves performance by an average of 23.93 F1 points over EM. Human validation further confirms REGen's effectiveness, achieving 87.67% alignment with human assessments of argument correctness.
View on arXiv@article{sharif2025_2502.16838,
title={ REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction },
author={ Omar Sharif and Joseph Gatto and Madhusudan Basak and Sarah M. Preum },
journal={arXiv preprint arXiv:2502.16838},
year={ 2025 }
}