M ozila : Continual Event Detection Through the Lens of Multi-Objective Optimization and Language Model Head Preservation

Abstract Continual Event Detection (CED) aims to identify and classify event triggers in evolving ontologies, where novel event types emerge incrementally. Unlike standard continual learning, CED faces highly imbalanced, long-tail distributions, semantic overlaps between event types, and a prevalence of negative spans, all of which exacerbate catastrophic forgetting. To address these challenges, we propose Mozila, a novel framework that integrates Sharpness-Aware Multi-Objective Optimization (SaMOO) and Language Model Head Preservation (LMHP). SaMOO adapts gradient-based multi-objective optimization with sharpness-aware minimization to balance competing objectives, navigate the Pareto front, and locate flatter minima, enhancing robustness across sequential tasks. Meanwhile, LMHP strengthens representation learning by preserving essential linguistic knowledge from the pretrained language model head through distribution alignment, explored with three alternative strategies. To further address data imbalance, we leverage a generative model to synthesize event trigger representations, mitigating replay buffer limitations. Comprehensive experiments on standard CED benchmarks demonstrate that Mozila consistently improves performance, achieving a significant improvements over baseline methods. Ablation studies and error analyses highlight the individual contributions of each component and reveal key challenges in CED, including class imbalance and distinguishing similar event types across tasks. Our framework provides a robust and generalizable solution for continual event detection in dynamic, real-world environments. All resources are publicly available at: Mozila.