Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs). However, existing PLM-based methods ignore the information of trigger/argument fields, which is crucial for understanding event schemas. To this end, we propose a Probabilistic reCoupling model enhanced Event extraction framework (ProCE). Specifically, we first model the syntactic-related event fields as probabilistic biases, to clarify the event fields from ambiguous entanglement. Furthermore, considering multiple occurrences of the same triggers/arguments in EE, we explore probabilistic interaction strategies among multiple fields of the same triggers/arguments, to recouple the corresponding clarified distributions and capture more latent information fields. Experiments on EE datasets demonstrate the effectiveness and generalization of our proposed approach.

翻译：事件抽取（EE）旨在从事件提及中识别并分类事件触发词和论元，该任务已从预训练语言模型（PLM）中获益。然而，现有基于PLM的方法忽略了触发词/论元场域信息，而这类信息对理解事件模式至关重要。为此，我们提出了一种增强型事件抽取框架——概率重耦合模型（ProCE）。具体而言，我们首先将句法相关的事件场域建模为概率偏差，以从模糊纠缠中厘清事件场域。进一步地，考虑到事件抽取中相同触发词/论元的多重出现情况，我们探索了同一触发词/论元多个场域间的概率交互策略，以重耦合对应的厘清分布并捕获更多潜在场域信息。在事件抽取数据集上的实验证明了所提方法的有效性和泛化能力。