  Event Argument Extraction (EAE) is an extremely difficult information
extraction problem -- with significant limitations in few-shot cross-domain
(FSCD) settings. A common solution to FSCD modeling is data augmentation.
Unfortunately, existing augmentation methods are not well-suited to a variety
of real-world EAE contexts including (i) The need to model long documents (10+
sentences) (ii) The need to model zero and few-shot roles (i.e. event roles
with little to no training representation). In this work, we introduce two
novel LLM-powered data augmentation frameworks for synthesizing extractive
document-level EAE samples using zero in-domain training data. Our highest
performing methods provide a 16-pt increase in F1 score on extraction of zero
shot role types.
  To better facilitate analysis of cross-domain EAE, 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 LLM-based augmentation
can boost RDF1 performance by up to 11 F1 points compared to baseline methods.
