A Joint Event Extraction Method Based on Curriculum Adversarial Learning and Adaptive Enhancement

Event extraction is a core NLP task that aims to identify triggers and arguments in unstructured text. In the financial domain, dense events, overlapping arguments, and ambiguous semantics pose significant challenges. This paper proposes CADAEE, a joint extraction framework that integrates curriculum adversarial learning and an enhanced adaptive layer. Curriculum adversarial learning dynamically adjusts training difficulty, thereby improving robustness and generalization on complex samples. The enhanced adaptive layer introduces learnable role-bias embeddings to model semantic dependencies between triggers and arguments, while a multi-head attention mechanism captures diverse feature interactions. Extensive experiments on the FewFC and DuEE-Fin datasets demonstrate the superiority of CADAEE. The model achieves highly competitive F1-scores in both trigger and argument classification, reaching 80.1% and 73.5% on FewFC, and 88.8% and 71.8% on DuEE-Fin, respectively. Ablation studies validate the synergistic contributions of the proposed modules. These results demonstrate that CADAEE provides robust and accurate extraction in complex, overlapping event scenarios, highlighting the value of combining curriculum learning with adaptive, role-aware enhancements for financial event extraction.