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 https://github.com/Stardust-hyx/TabEAE.

翻译：事件共现在先前的事件抽取（Event Extraction, EE）研究中被证明是有效的，但近期的事件论元抽取（Event Argument Extraction, EAE）研究并未充分考虑该因素。本文旨在填补EE研究与EAE研究之间的这一空白，聚焦于一个核心问题：“当感知到事件共现时，EAE模型能否学得更好？”为回答该问题，我们将EAE重新建模为表格生成问题，并将一个基于提示的SOTA EAE模型扩展为名为TabEAE的非自回归生成框架，该框架能够并行抽取多个事件中的论元。在此框架下，我们在4个数据集（ACE05、RAMS、WikiEvents和MLEE）上实验了3种不同的训练-推理方案，并发现通过以并行方式训练模型抽取所有事件，模型能够更好地区分各事件的语义边界，其单个事件的抽取能力也得到显著提升。实验结果表明，我们的方法在4个数据集上均取得了新的最佳性能。我们的代码已开源在https://github.com/Stardust-hyx/TabEAE。