AssocKD: An Association-Aware Knowledge Distillation Method for Document-Level Event Argument Extraction

Lijun Tan, Yanli Hu (), Jianwei Cao and Zhen Tan
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Lijun Tan: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Yanli Hu: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Jianwei Cao: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Zhen Tan: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2024, vol. 12, issue 18, 1-20

Abstract: Event argument extraction is a crucial subtask of event extraction, which aims at extracting arguments that correspond to argument roles when given event types. The majority of current document-level event argument extraction works focus on extracting information for only one event at a time without considering the association among events; this is known as document-level single-event extraction. However, the interrelationship among arguments can yield mutual gains in their extraction. Therefore, in this paper, we propose AssocKD, an Association-aware Knowledge Distillation Method for Document-level Event Argument Extraction, which enables the enhancement of document-level multi-event extraction with event association knowledge. Firstly, we introduce an association-aware training task to extract unknown arguments with the given privileged knowledge of relevant arguments, obtaining an association-aware model that can construct both intra-event and inter-event relationships. Secondly, we adopt multi-teacher knowledge distillation to transfer such event association knowledge from the association-aware teacher models to the event argument extraction student model. Our proposed method, AssocKD, is capable of explicitly modeling and efficiently leveraging event association to enhance the extraction of multi-event arguments at the document level. We conduct experiments on RAMS and WIKIEVENTS datasets and observe a significant improvement, thus demonstrating the effectiveness of our method.

Keywords: document-level event argument extraction; multi-event argument extraction; association-aware construction; knowledge distillation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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