Document-Level Event Argument Extraction (DocEAE) is an extremely difficult information extraction problem -- with significant limitations in low-resource cross-domain settings. To address this problem, we introduce Mad Lib Aug (MLA), a novel generative DocEAE data augmentation framework. Our approach leverages the intuition that Mad Libs, which are categorically masked documents used as a part of a popular game, can be generated and solved by LLMs to produce data for DocEAE. Using MLA, we achieve a 2.6-point average improvement in overall F1 score. Moreover, this approach achieves a 3.9 and 5.2 point average increase in zero and few-shot event roles compared to augmentation-free baselines across all experiments. To better facilitate analysis of cross-domain DocEAE, we additionally introduce a new metric, Role-Depth F1 (RDF1), which uses statistical depth to identify roles in the target domain which are semantic outliers with respect to roles observed in the source domain. Our experiments show that MLA augmentation can boost RDF1 performance by an average of 5.85 points compared to non-augmented datasets.

利用Mad Lib Aug (MLA)生成的数据，可以提高文档级事件论点提取的性能，特别是在低资源跨领域环境下，并且引入了Role-Depth F1（RDF1）作为新的度量指标来衡量不同领域中语义异常的角色。

仅需Mad Libs：增强跨领域文档级事件参数数据