Learning to Ask for Data-Efficient Event Argument Extraction (Student Abstract)
Keywords:Prompt, Event Argument Extraction, Event Extraction
AbstractEvent 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
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