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.

翻译：先前研究表明，将序贯潜变量模型与语义本体知识相结合能够提升事件建模方法的表征能力。本文提出了一种新颖的双层分层半监督事件建模框架，该框架在实现结构层次性的同时兼顾本体层次性。我们的方法包含多层结构化潜变量，每一层都对前一层进行压缩与抽象。我们通过注入定义在事件类型层面的结构化本体知识来引导这一压缩过程：关键在于，本模型支持语义知识的部分注入，且不依赖于语义本体特定层级的实例观测。在两个不同数据集和四项评估指标上，我们证明该方法相较以往最先进方法性能提升高达8.5%，充分体现了结构化语义分层知识对事件建模的促进作用。