OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event
  Understanding
Hao Peng Xiaozhi Wang Feng Yao Zimu Wang Chuzhao Zhu Kaisheng Zeng Lei Hou Juanzi Li
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/).