Learning to Ask for Data-Efficient Event Argument Extraction (Student Abstract)


  • Hongbin Ye Zhejiang University
  • Ningyu Zhang Zhejiang University
  • Zhen Bi Zhejiang University
  • Shumin Deng Zhejiang University
  • Chuanqi Tan Alibaba Group
  • Hui Chen Alibaba Group
  • Fei Huang Alibaba Group
  • Huajun Chen Zhejiang University




Prompt, Event Argument Extraction, Event Extraction


Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and empirically analyze fixed discrete token template performance. As generating human-annotated question templates is often time-consuming and labor-intensive, we further propose a novel approach called “Learning to Ask,” which can learn optimized question templates for EAE without human annotations. Experiments using the ACE-2005 dataset demonstrate that our method based on optimized questions achieves state-of-the-art performance in both the few-shot and supervised settings.




How to Cite

Ye, H., Zhang, N., Bi, Z., Deng, S., Tan, C., Chen, H., Huang, F., & Chen, H. (2022). Learning to Ask for Data-Efficient Event Argument Extraction (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13099-13100. https://doi.org/10.1609/aaai.v36i11.21686