Semantically-informed Hierarchical Event Modeling (2023)

Abstract: 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 consistsof 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.

## introduction intuitively, there is a hierarchical nature to complex events: e.g., on fig. 1 , there are two events, one involves going to the hospital and another one is getting treatment. even if important portions may differ, but these two situations have one abstract concept in common: cure (of a disease). clearly, there is a connection among the events reported in a situation and they all contribute to a bigger goal ("cure" in this case). the main purpose of our work is to exploit this nature of connection to improve event modeling. however, much like linguistic structure, this event structure is generally not directly observed, making it difficult to learn event models that reflect this hierarchical nature. ## method our core aim is to provide a hierarchical event model that incorporates both structural and semantic hierarchy. we call our model shem (semantically-informed hierarchical event modeling). an overview is in fig. 2 , where an observed event sequence (green x i ) is latently modeled as multiple sequences of semantic frames (f i and h j ), augmented by a semantic resource. we examine the strengths and limitations of structural and semantic hierarchy. our experiments explore the effect of compressing the number of frames on ability to predict what happens next in an event sequence, and, given an initial seed event, how an event sequence is likely to unfold. we also extend our work to show how our model can produce better intrinsic event representations. ## experimental setup we describe the dataset, then baselines ( §4.1), we used for our core experiments. we explored the effectiveness of latent parent frames ( §5.1) and frame relations ( §5.2). we show how our model accounts for missing events ( §5.3). to further show the effectiveness of our model, we show how to extend our approach to provide effective representations for event similarity tasks ( §5.4). we provide supplementary results and experiments in the appendix. ## result and discussion we compute standard event modeling metrics: perplexity, to measure how well the model can predict the next event, and inverse narrative cloze (inc) score @xcite . in inc, a single seed event is given, and the model must select what the next five events are to follow it. the model is given six choices (giving random performance accuracy of 16.7%). both have been used by our baselines and allow us to assess the effectiveness of our model. we average results over four runs with different seeds, unless otherwise specified. ## conclusion we have presented a hierarchical event model that accounts for both structural and ontological hierarchy across an event sequence. we use automatically extracted semantic frames to guide the first level of concept, and then use framenet relations to guide abstraction and generalization. we showed improvements across multiple tasks and evaluation measures within event modeling. we showed improvements in next event prediction, longer range event prediction, missing event regeneration, and event similarity. we believe that future work can use this abstraction concept for summarization, topic modeling, or other downstream tasks.