  Recent works show that the graph structure of sentences, generated from
dependency parsers, has potential for improving event detection. However, they
often only leverage the edges (dependencies) between words, and discard the
dependency labels (e.g., nominal-subject), treating the underlying graph edges
as homogeneous. In this work, we propose a novel framework for incorporating
both dependencies and their labels using a recently proposed technique called
Graph Transformer Networks (GTN). We integrate GTNs to leverage dependency
relations on two existing homogeneous-graph-based models, and demonstrate an
improvement in the F1 score on the ACE dataset.
