Learning to Ask for Data-Efficient Event Argument Extraction

• URL: http://arxiv.org/abs/2110.00479v1
• Date: Fri, 1 Oct 2021 15:22:37 GMT
• Title: Learning to Ask for Data-Efficient Event Argument Extraction
• Authors: Hongbin Ye, Ningyu Zhang, Zhen Bi, Shumin Deng, Chuanqi Tan, Hui Chen, Fei Huang, Huajun Chen
• Abstract summary: 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. We propose a novel approach called "Learning to Ask," which can learn optimized question templates for EAE without human annotations.
• Score: 23.106166629659405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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