mlsanityData to 14 Oct 2026

Event Extraction as Question Generation and Answering

arXiv 2307.05567 ↗First posted 10 Jul 2023cs.CLcited by 16

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Abstract (arXiv)

Recent work on Event Extraction has reframed the task as Question Answering (QA), with promising results. The advantage of this approach is that it addresses the error propagation issue found in traditional token-based classification approaches by directly predicting event arguments without extracting candidates first. However, the questions are typically based on fixed templates and they rarely leverage contextual information such as relevant arguments. In addition, prior QA-based approaches have difficulty handling cases where there are multiple arguments for the same role. In this paper, we propose QGA-EE, which enables a Question Generation (QG) model to generate questions that incorporate rich contextual information instead of using fixed templates. We also propose dynamic templates to assist the training of QG model. Experiments show that QGA-EE outperforms all prior single-task-based models on the ACE05 English dataset.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 15 of its 35 references that are in the map. First posted on arXiv 10 Jul 2023.

Builds on

15 of 35 references.

References · 15

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  4. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  5. 2018Adafactor: Adaptive Learning Rates with Sublinear Memory Costcited by 905
  6. 2019Entity, Relation, and Event Extraction with Contextualized Span Representationscited by 262
  7. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  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. 2021DEGREE: A Data-Efficient Generation-Based Event Extraction Modelcited by 83
  12. 2019Reading the Manual: Event Extraction as Definition Comprehensioncited by 26
3 more
  1. 2021Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networkscited by 22
  2. 2020Resource-Enhanced Neural Model for Event Argument Extractioncited by 15
  3. 2020Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extractioncited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 8; 2025: 3; 2026: 2.

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  1. 2023Large Language Models for Generative Information Extraction: A Surveycited by 185
  2. 2024Large Language Models Meet NLP: A Surveycited by 103
  3. 2024Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extractioncited by 33
  4. 2023TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extractioncited by 28
  5. 2024Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Argumentscited by 9
  6. 2023MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotationcited by 6
  7. 2024CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military Newscited by 6
  8. 2024Towards Better Question Generation in QA-based Event Extractioncited by 6
  9. 2025Event Extraction in Large Language Modelcited by 5
  10. 2025REGen: A Reliable Evaluation Framework for Generative Event Argument Extractioncited by 4
  11. 2024On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extractioncited by 3
  12. 2024DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Queryingcited by 3
4 more
  1. 2025LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real Worldcited by 2
  2. 2026Improving Information Extraction with Learned Queriescited by 1
  3. 2024CEHA: A Dataset of Conflict Events in the Horn of Africacited by 0
  4. 2026CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotationcited by 0