Numerous events occur worldwide and are documented in the news, social media,
and various online platforms in raw text. Extracting useful and succinct
information about these events is crucial to various downstream applications.
Event Argument Extraction (EAE) deals with the task of extracting
event-specific information from natural language text. In order to cater to new
events and domains in a realistic low-data setting, there is a growing urgency
for EAE models to be generalizable. Consequentially, there is a necessity for
benchmarking setups to evaluate the generalizability of EAE models. But most
existing benchmarking datasets like ACE and ERE have limited coverage in terms
of events and cannot adequately evaluate the generalizability of EAE models. To
alleviate this issue, we introduce a new dataset GENEVA covering a diverse
range of 115 events and 187 argument roles. Using this dataset, we create four
benchmarking test suites to assess the model's generalization capability from
different perspectives. We benchmark various representative models on these
test suites and compare their generalizability relatively. Finally, we propose
a new model SCAD that outperforms the previous models and serves as a strong
benchmark for these test suites.

该研究介绍了一个新的数据集 GENEVA，涵盖了 115 个事件和 187 个参数角色，用于评估不同方面的模型泛化能力，并比较不同模型的相对普适性，最后提出了 SCAD 新模型，其性能优于以前的模型，并为这些测试套件提供强大的基准。