Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction
Lu Dai, Bang Wang, Wei Xiang, Yijun Mo · 2022
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 bidirectional iterative prompt-tuning method for EAE, where the EAE task is treated as a clozestyle 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 clozestyle 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 stateof-the-art methods.Our code is available at https://github.com/HustMinsLab/BIP.