A Multi-Hop Graph Neural Network for Event Detection via a Stacked Module and a Feedback Network

Abstract

Event detection is an important subtask of information extraction, aiming to identify triggers and recognize event types in text. Previous state-of-the-art studies using graph neural networks (GNNs) are mainly applied to obtain long distance features of text and have achieved impressive performance. However, these methods face the issues of over-smoothing and semantic feature destruction, when containing multiple GNN layers. For the reasons, this paper proposes an improved GNN model for event detection. The model first proposes a stacked module to enrich node representation to alleviate the over-smoothing. The module aggregates multi-hop neighbors with different weights by stacking different GNNs in each hidden layer, so that the representation of nodes no longer tends to be similar. Then, a feedback network is designed with a gating mechanism to retain effective semantic information in the propagation process of the model. Finally, experimental results demonstrate that our model achieves competitive results in many indicators compared with state-of-the-art methods.