SEGCN-DCR: A syntax-enhanced event detection framework with decoupled classification rebalance

Abstract

Event Detection (ED) is a pivotal sub-task of Event Extraction(EE). It aims to locate triggers and categorize them into specific event types. Recent researches on ED have shown that graph convolutional neural networks with syntactic information can achieve advanced performance. However, these methods ignore the implicit importance score of tokens. This will weaken their ability of identifying trigger words. In addition, due to the long-tailed distribution in the corpus, previous methods perform poorly on sparsely labeled trigger words and are prone to overfitting on densely labeled ones. In this paper, we propose a Syntax-Enhanced GCN framework with a Decoupled Classification Rebalance mechanism (SEGCN-DCR) to address the above issues. Specifically, we exploit a tree-structured module based on dependency structure to reduce the noise by capturing global hierarchical syntactic information, and DCR mechanism to rescale the classifier weights, which makes classifier decision boundaries more reasonable. Experiments on benchmark ACE2005 show that the proposed method acquires state-of-the-art performance.