# Semantically-informed Hierarchical Event Modeling

arXiv: https://arxiv.org/abs/2212.10547 · HTML: https://mlsanity.com/paper/2212.10547/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [Reasoning about Goals, Steps, and Temporal Ordering with WikiHow](https://mlsanity.com/paper/2009.07690/) (2020)
- [Unified Semantic Typing with Meaningful Label Inference](https://mlsanity.com/paper/2205.01826/) (2022)
- ["What Are You Trying to Do?" Semantic Typing of Event Processes](https://mlsanity.com/paper/2010.06724/) (2020)
- [Probabilistic Frame Induction](https://mlsanity.com/paper/1302.4813/) (2013)
- [Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering](https://mlsanity.com/paper/2203.07633/) (2022)
- [Hierarchical Quantized Representations for Script Generation](https://mlsanity.com/paper/1808.09542/) (2018)
- [Event Representation with Sequential, Semi-Supervised Discrete Variables](https://mlsanity.com/paper/2010.04361/) (2020)
- [SURFACE: Semantically Rich Fact Validation with Explanations](https://mlsanity.com/paper/1810.13223/) (2018)
- [Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention](https://mlsanity.com/paper/2202.05943/) (2022)
- [Semantic Frame Forecast](https://mlsanity.com/paper/2104.05604/) (2021)
- [Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles](https://mlsanity.com/paper/1706.09562/) (2017)

## Built on by (most cited first, 2 of 2)

- [Human-Robot Dialogue Annotation for Multi-Modal Common Ground](https://mlsanity.com/paper/2411.12829/) (2024)
- [OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution](https://mlsanity.com/paper/2609.06490/) (2026)

## Abstract (arXiv)

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.