Event understanding aims at understanding the content and relationship of events within texts, which covers multiple complicated information extraction tasks: event detection, event argument extraction, and event relation extraction. To facilitate related research and application, we present an event understanding toolkit OmniEvent, which features three desiderata: (1) Comprehensive. OmniEvent supports mainstream modeling paradigms of all the event understanding tasks and the processing of 15 widely-used English and Chinese datasets. (2) Fair. OmniEvent carefully handles the inconspicuous evaluation pitfalls reported in Peng et al. (2023), which ensures fair comparisons between different models. (3) Easy-to-use. OmniEvent is designed to be easily used by users with varying needs. We provide off-the-shelf models that can be directly deployed as web services. The modular framework also enables users to easily implement and evaluate new event understanding models with OmniEvent. The toolkit (https://github.com/THU-KEG/OmniEvent) is publicly released along with the demonstration website and video (https://omnievent.xlore.cn/).

翻译：事件理解旨在理解文本中事件的内容与关系，涵盖多项复杂的信息抽取任务：事件检测、事件论元抽取和事件关系抽取。为促进相关研究与应用，我们提出事件理解工具包OmniEvent，其具备三个关键特性：（1）全面性。OmniEvent支持所有事件理解任务的主流建模范式，并处理15个广泛使用的英文和中文数据集。（2）公平性。OmniEvent精心处理了Peng等人（2023）报告中指出的不易察觉的评估陷阱，确保不同模型之间的公平比较。（3）易用性。OmniEvent设计便于满足不同用户的需求。我们提供可直接部署为Web服务的现成模型。此外，其模块化框架使用户能够借助OmniEvent轻松实现并评估新的事件理解模型。该工具包（https://github.com/THU-KEG/OmniEvent）已公开发布，同步提供演示网站与视频（https://omnievent.xlore.cn/）。