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

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

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 toolkit1 is publicly released along with the demonstration website and video.

Original languageEnglish
Pages508-517
Number of pages10
Publication statusPublished - 2023
Event2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Singapore, Singapore
Duration: 6 Dec 202310 Dec 2023

Conference

Conference2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
Country/TerritorySingapore
CitySingapore
Period6/12/2310/12/23

Fingerprint

Dive into the research topics of 'OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding'. Together they form a unique fingerprint.

Cite this