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

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.

Authors

• Hongbin Ye
• Ningyu Zhang Zhejiang University Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph
• Zhen Bi Zhejiang University Zhejiang University - Ant Group Joint Laboratory of Knowledge Graph
• Shumin Deng NUS-NCS Joint Lab, National University of Singapore
• Chuanqi Tan
• Hui Chen
• Fei Huang Tongyi Lab
• Huajun Chen College of Computer Science and Technology, Zhejiang University ZJU-Ant Group Joint Lab of Knowledge Graph Zhejiang Key Laboratory of Big Data Intelligent Computing

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