  Prior work has shown that coupling sequential latent variable models with
semantic ontological knowledge can improve the representational capabilities of
event modeling approaches. In this work, we present a novel, doubly
hierarchical, semi-supervised event modeling framework that provides structural
hierarchy while also accounting for ontological hierarchy. Our approach
consists of multiple layers of structured latent variables, where each
successive layer compresses and abstracts the previous layers. We guide this
compression through the injection of structured ontological knowledge that is
defined at the type level of events: importantly, our model allows for partial
injection of semantic knowledge and it does not depend on observing instances
at any particular level of the semantic ontology. Across two different datasets
and four different evaluation metrics, we demonstrate that our approach is able
to out-perform the previous state-of-the-art approaches by up to 8.5%,
demonstrating the benefits of structured and semantic hierarchical knowledge
for event modeling.
