BNU: A Balance-Normalization-Uncertainty Model for Incremental Event Detection

Event detection is challenging in real-world application since new events continually occur and old events still exist which may result in repeated labeling for old events. Therefore, incremental event detection is essential where a model continuously learns new events and meanwhile prevents performance from degrading on old events. Although existing incremental event detection models achieve impressive performance, they face the data imbalance problem between old classes and new classes, and have the knowledge transfer problem which cannot adequately utilize the knowledge provided by the previous model and data. To this end, we propose a Balance-Normalization-Uncertainty (BNU) model to address above problems. Specifically, in order to mitigate the adverse effects of data imbalance, we incorporate a balanced fine-tuning stage and a cosine normalization module. Meanwhile, we consider aleatoric uncertainty to preserve previous knowledge while training for new events. Experimental results show that our proposed method resolves the above challenges effectively and achieves consistent and significant performance on ACE and TAC KBP datasets.