  Recent studies on event detection (ED) haveshown that the syntactic
dependency graph canbe employed in graph convolution neural net-works (GCN) to
achieve state-of-the-art per-formance. However, the computation of thehidden
vectors in such graph-based models isagnostic to the trigger candidate words,
po-tentially leaving irrelevant information for thetrigger candidate for event
prediction. In addi-tion, the current models for ED fail to exploitthe overall
contextual importance scores of thewords, which can be obtained via the
depen-dency tree, to boost the performance. In thisstudy, we propose a novel
gating mechanismto filter noisy information in the hidden vec-tors of the GCN
models for ED based on theinformation from the trigger candidate. Wealso
introduce novel mechanisms to achievethe contextual diversity for the gates and
theimportance score consistency for the graphsand models in ED. The experiments
show thatthe proposed model achieves state-of-the-artperformance on two ED
datasets
