@inproceedings{peng-etal-2023-omnievent,
title = "{O}mni{E}vent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding",
author = "Peng, Hao and
Wang, Xiaozhi and
Yao, Feng and
Wang, Zimu and
Zhu, Chuzhao and
Zeng, Kaisheng and
Hou, Lei and
Li, Juanzi",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.46",
doi = "10.18653/v1/2023.emnlp-demo.46",
pages = "508--517",
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 is publicly released along with the demonstration website and video.",
}
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<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 is publicly released along with the demonstration website and video.</abstract>
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%0 Conference Proceedings
%T OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding
%A Peng, Hao
%A Wang, Xiaozhi
%A Yao, Feng
%A Wang, Zimu
%A Zhu, Chuzhao
%A Zeng, Kaisheng
%A Hou, Lei
%A Li, Juanzi
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F peng-etal-2023-omnievent
%X 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 is publicly released along with the demonstration website and video.
%R 10.18653/v1/2023.emnlp-demo.46
%U https://aclanthology.org/2023.emnlp-demo.46
%U https://doi.org/10.18653/v1/2023.emnlp-demo.46
%P 508-517
Markdown (Informal)
[OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding](https://aclanthology.org/2023.emnlp-demo.46) (Peng et al., EMNLP 2023)
ACL
- Hao Peng, Xiaozhi Wang, Feng Yao, Zimu Wang, Chuzhao Zhu, Kaisheng Zeng, Lei Hou, and Juanzi Li. 2023. OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 508–517, Singapore. Association for Computational Linguistics.