Event co-occurrences have been proved effective for event extraction (EE) in
previous studies, but have not been considered for event argument extraction
(EAE) recently. In this paper, we try to fill this gap between EE research and
EAE research, by highlighting the question that ``Can EAE models learn better
when being aware of event co-occurrences?''. To answer this question, we
reformulate EAE as a problem of table generation and extend a SOTA prompt-based
EAE model into a non-autoregressive generation framework, called TabEAE, which
is able to extract the arguments of multiple events in parallel. Under this
framework, we experiment with 3 different training-inference schemes on 4
datasets (ACE05, RAMS, WikiEvents and MLEE) and discover that via training the
model to extract all events in parallel, it can better distinguish the semantic
boundary of each event and its ability to extract single event gets
substantially improved. Experimental results show that our method achieves new
state-of-the-art performance on the 4 datasets. Our code is avilable at
this https URL

本文主要介绍了通过利用事件共现特性可以提高事件提取以及事件参数提取质量，提出了一种基于表格生成的非自回归框架 TabEAE，并在 4 个数据集上进行了 3 种不同的训练推理方案实验，结果表明该方法在 4 个数据集上均达到了最优的性能表现。