  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.
