mlsanityData to 7 Oct 2026

Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction

arXiv 2404.04770 ↗First posted 7 Apr 2024cs.CLcited by 5

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Abstract (arXiv)

This paper presents multiple question generation strategies for document-level event argument extraction. These strategies do not require human involvement and result in uncontextualized questions as well as contextualized questions grounded on the event and document of interest. Experimental results show that combining uncontextualized and contextualized questions is beneficial, especially when event triggers and arguments appear in different sentences. Our approach does not have corpus-specific components, in particular, the question generation strategies transfer across corpora. We also present a qualitative analysis of the most common errors made by our best model.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 53 references that are in the map. First posted on arXiv 7 Apr 2024.

Builds on

15 of 53 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  6. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 858
  7. 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
  8. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 154
  9. 2021Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue Systemcited by 139
  10. 2022Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extractioncited by 54
  11. 2021ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoningcited by 30
  12. 2022Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learningcited by 25
3 more
  1. 2022Retrieval-Augmented Generative Question Answering for Event Argument Extractioncited by 18
  2. 2023Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?cited by 10
  3. 2023An AMR-based Link Prediction Approach for Document-level Event Argument Extractioncited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2025Event Extraction in Large Language Modelcited by 5
  2. 2024DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Queryingcited by 3
  3. 2025LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real Worldcited by 2
  4. 2026KARMA: Knowledge graph-based Automated Reasoning Materialization and Alignmentcited by 0
  5. 2026Improving Information Extraction with Learned Queriescited by 0