When Modernity Enhances Tradition and Specialization: A BERT Ensemble for Temporal Financial Argument Detection - Martinho, Leonardo; Dutra, Hugo; Assis, Gabriel; Carvalho, Jonnathan; Paes, Aline (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2025: Anais do XIII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2025.247741, 2025)
Temporal references in financial texts, such as earnings conference calls (ECCs), are essential for interpreting corporate discourse and guiding informed investment decisions. However, accurately identifying these references remains a challenge due to domain-specific language and vague temporal cues. In this work, we investigate whether combining distinct BERT-based models can improve performance on temporal financial argument detection. We evaluate an ensemble approach built upon three complementary models: BERT, FinBERT, and ModernBERT. Experiments on the FinArg-2 ECC dataset show that, while ModernBERT performs best individually, the ensemble of all three models — using a soft voting strategy — sets a new state of the art. These results highlight the potential of BERT-based ensembles for more accurate and robust temporal reasoning in financial NLP tasks.