Event Detection With Dynamic Word-Trigger-Argument Graph Neural Networks

The task of ACE Event Detection (ED) often encounters ambiguous and unseen trigger words. Most conventional ED systems exclusively consider the semantic or syntactic patterns as the additional evidence to resolve the problem of the ambiguous and unseen triggers, but rarely consider taking advantages of structured knowledge of the event itself. In this study, we propose Dynamic Word-Trigger-Argument Graph Neural Networks (DWTA-GNN), a novel framework that leverages event structure knowledge to facilitate the two issues simultaneously. In our approach, we utilize words, entities, and event annotations from training to construct an event background graph, which can provide sufficient information of event structure to better disambiguate polysemous triggers and identify unseen triggers. To make full use of the constructed background graph, we further design a knowledge matching module to dynamically match appropriate event structure knowledge and construct a subgraph for each incoming sentence. Besides, an event-selective graph convolution is applied to filter out the noise in the matched knowledge so as to enhance event representation. Experiments on the ACE2005 dataset show that our model achieves competitive performance and advances previous approaches on ambiguous and unseen trigger words, verifying the effectiveness of incorporating event structure knowledge for event detection.