mlsanityData to 14 Oct 2026

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

arXiv 2309.14258 ↗First posted 25 Sep 2023cs.CL cs.AIcited by 11

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Abstract (arXiv)

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/).

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 26 of its 55 references that are in the map. First posted on arXiv 25 Sep 2023.

Builds on

26 of 55 references.

References · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  5. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  6. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  7. 2019Entity, Relation, and Event Extraction with Contextualized Span Representationscited by 262
  8. 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
  9. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 155
  10. 2021Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extractioncited by 129
  11. 2020MAVEN: A Massive General Domain Event Detection Datasetcited by 104
  12. 2019Multi-Sentence Argument Linkingcited by 81
14 more
  1. 2018A Multi-Axis Annotation Scheme for Event Temporal Relationscited by 75
  2. 2022MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extractioncited by 66
  3. 2019Joint Reasoning for Temporal and Causal Relationscited by 62
  4. 2022Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extractioncited by 54
  5. 2019Joint Event and Temporal Relation Extraction with Shared Representations and Structured Predictioncited by 53
  6. 2020Joint Constrained Learning for Event-Event Relation Extractioncited by 53
  7. 2021CLEVE: Contrastive Pre-training for Event Extractioncited by 36
  8. 2022LEVEN: A Large-Scale Chinese Legal Event Detection Datasetcited by 36
  9. 2021Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networkscited by 22
  10. 2023The Devil is in the Details: On the Pitfalls of Event Extraction Evaluationcited by 19
  11. 2021EventPlus: A Temporal Event Understanding Pipelinecited by 17
  12. 2022DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Populationcited by 17
  13. 2023More than Classification: A Unified Framework for Event Temporal Relation Extractioncited by 15
  14. 2023Zero- and Few-Shot Event Detection via Prompt-Based Meta Learningcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 2; 2026: 2.

11 papers.

  1. 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Taskscited by 37
  2. 2023TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extractioncited by 28
  3. 2024Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answeringcited by 12
  4. 2023MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotationcited by 6
  5. 2024CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military Newscited by 6
  6. 2025Event Extraction in Large Language Modelcited by 5
  7. 2025Benchmarking and Improving LVLMs on Event Extraction from Multimedia Documentscited by 3
  8. 2024Argument-Aware Approach To Event Linkingcited by 1
  9. 2024Beyond Exact Match: Semantically Reassessing Event Extraction by Large Language Modelscited by 0
  10. 2026NEST: Narrative Event Structures in Time for Long Video Understandingcited by 0
  11. 2026Evaluation Pitfalls and Challenges in Multimedia Event Extractioncited by 0