Recently, prompt-tuning has attracted growing interests in event argument
extraction (EAE). However, the existing prompt-tuning methods have not achieved
satisfactory performance due to the lack of consideration of entity
information. In this paper, we propose a bi-directional iterative prompt-tuning
method for EAE, where the EAE task is treated as a cloze-style task to take
full advantage of entity information and pre-trained language models (PLMs).
Furthermore, our method explores event argument interactions by introducing the
argument roles of contextual entities into prompt construction. Since template
and verbalizer are two crucial components in a cloze-style prompt, we propose
to utilize the role label semantic knowledge to construct a semantic verbalizer
and design three kinds of templates for the EAE task. Experiments on the ACE
2005 English dataset with standard and low-resource settings show that the
proposed method significantly outperforms the peer state-of-the-art methods.
Our code is available at this https URL.

本文提出了一种双向迭代 prompt-tuning 方法用于事件论元提取，将该任务视为填空式任务以充分利用实体信息和预训练语言模型（PLMs），通过引入上下文实体的论据角色来探索事件论证交互，并利用角色标签语义知识构建语义化言化器并为 EAE 任务设计三种模板，在 ACE 2005 英文数据集上进行有效性实验。