Continual Event Extraction with Semantic Confusion Rectification

Zitao Wang, Xinyi Wang, Wei Hu

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Keywords: Event extraction, Continual learning, Semantic confusion, Knowledge transfer

Abstract: 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.

Submission Number: 2310