  Event argument extraction has long been studied as a sequential prediction
problem with extractive-based methods, tackling each argument in isolation.
Although recent work proposes generation-based methods to capture
cross-argument dependency, they require generating and post-processing a
complicated target sequence (template). Motivated by these observations and
recent pretrained language models' capabilities of learning from
demonstrations. We propose a retrieval-augmented generative QA model (R-GQA)
for event argument extraction. It retrieves the most similar QA pair and
augments it as prompt to the current example's context, then decodes the
arguments as answers. Our approach outperforms substantially prior methods
across various settings (i.e. fully supervised, domain transfer, and fewshot
learning). Finally, we propose a clustering-based sampling strategy (JointEnc)
and conduct a thorough analysis of how different strategies influence the
few-shot learning performance. The implementations are available at https://
github.com/xinyadu/RGQA
