Recouple Event Field via Probabilistic Bias for Event Extraction
Xingyu Bai Taiqiang Wu Han Guo Zhe Zhao Xuefeng Yang Jiayi Li Weijie Liu Qi Ju Weigang Guo Yujiu Yang
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.