OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding

• URL: http://arxiv.org/abs/2309.14258v1
• Date: Mon, 25 Sep 2023 16:15:09 GMT
• Title: OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding
• Authors: Hao Peng, Xiaozhi Wang, Feng Yao, Zimu Wang, Chuzhao Zhu, Kaisheng Zeng, Lei Hou, Juanzi Li
• Abstract summary: Event understanding aims at understanding the content and relationship of events within texts. To facilitate related research and application, we present an event understanding toolkit OmniEvent.
• Score: 53.23073872040206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/).

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