Argument centric causal intervention for cross document event coreference resolution

Cross-document event coreference resolution (CD-ECR) is a fundamental task in natural language processing (NLP) that seeks to determine whether event mentions across multiple documents refer to the same real-world occurrence. However, current CD-ECR approaches predominantly rely on trigger features within input mention pairs, which induce spurious correlations between surface-level lexical features and coreference relationships, impairing the overall performance of the models. To address this issue, we propose a novel cross-document event coreference resolution method based on Argument-Centric Causal Intervention (ACCI). Specifically, we construct a structural causal graph to uncover confounding dependencies between lexical triggers and coreference labels, and introduce backdoor-adjusted interventions to isolate the true causal effect of argument semantics. To further mitigate spurious correlations, ACCI integrates a counterfactual reasoning module that quantifies the causal influence of trigger word perturbations, and an argument-aware enhancement module to promote greater sensitivity to semantically grounded information. Within a unified end-to-end framework, ACCI delivers reliable support for coreference decisions. Extensive experiments demonstrate that ACCI achieves state-of-the-art performance, with CoNLL F1 scores of 88.4% on ECB+ and 85.2% on GVC. The implementation and materials are available at https://github.com/era211/ACCI .