We consider dyadic zero-shot event extraction (EE) to identify actions
between pairs of actors. The \emph{zero-shot} setting allows social scientists
or other non-computational researchers to extract any customized,
user-specified set of events without training, resulting in a \emph{dyadic}
event database, allowing insight into sociopolitical relational dynamics among
actors and the higher level organizations or countries they represent.
Unfortunately, we find that current zero-shot EE methods perform poorly for the
task, with issues including word sense ambiguity, modality mismatch, and
efficiency. Straightforward application of large language model prompting
typically performs even worse. We address these challenges with a new
fine-grained, multi-stage generative question-answer method, using a Monte
Carlo approach to exploit and overcome the randomness of generative outputs. It
performs 90\% fewer queries than a previous approach, with strong performance
on the widely-used Automatic Content Extraction dataset. Finally, we extend our
method to extract affiliations of actor arguments and demonstrate our method
and findings on a dyadic international relations case study.

本研究提出了一种新的细粒度、多阶段的生成式问答方法，利用蒙特卡洛方法克服生成式输出的随机性，并将其应用于事件抽取和演员关系提取领域，同时在自动内容提取数据集上表现较强。