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

Hongbin Ye, Ningyu Zhang, Zhen Bi, Shumin Deng, Chuanqi Tan, Hui Chen, Fei Huang, Huajun Chen

[AAAI-22] Student Abstract and Poster Program
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.

Sessions where this paper appears

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    Fri, February 25 12:45 AM - 2:30 AM (+00:00)
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  • Poster Session 7

    Sat, February 26 4:45 PM - 6:30 PM (+00:00)
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