OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding
This toolkit addresses the need for a comprehensive and fair tool for researchers and practitioners working on event understanding tasks, though it is incremental as it builds on existing paradigms and datasets.
The authors tackled the challenge of event understanding in texts by developing OmniEvent, a toolkit that supports multiple tasks and datasets, resulting in a publicly released resource with off-the-shelf models and fair evaluation handling.
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/).