  We study continual event extraction, which aims to extract incessantly
emerging event information while avoiding forgetting. We observe that the
semantic confusion on event types stems from the annotations of the same text
being updated over time. The imbalance between event types even aggravates this
issue. This paper proposes a novel continual event extraction model with
semantic confusion rectification. We mark pseudo labels for each sentence to
alleviate semantic confusion. We transfer pivotal knowledge between current and
previous models to enhance the understanding of event types. Moreover, we
encourage the model to focus on the semantics of long-tailed event types by
leveraging other associated types. Experimental results show that our model
outperforms state-of-the-art baselines and is proficient in imbalanced
datasets.
