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.

Semantically-informed Hierarchical Event Modeling

The existence of external ("side”) semantic knowledge has been shown to result in more expressive computational event models. To enable the use of side information that may be noisy or missing, we propose a semi-supervised information bottleneck-based discrete latent variable model. We reparameterize the model's discrete variables with auxiliary continuous latent variables and a light-weight hierarchical structure. Our model is learned to minimize the mutual information between the observed data and optional side knowledge that is not already captured by the new, auxiliary variables. We theoretically show that our approach generalizes past approaches, and perform an empirical case study of our approach on event modeling. We corroborate our theoretical results with strong empirical experiments, showing that the proposed method outperforms previous proposed approaches on multiple datasets.

RevUp: Revise and Update Information Bottleneck for Event Representation

Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencoder, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semisupervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction but also converges more quickly.

Event Representation with Sequential, Semi-Supervised Discrete Variables

### [Program](https://sites.google.com/view/starsem2023/schedule)
To view Day 2 Live Session Recording click [**here**](https://underline.io/events/395/sessions?eventSessionId=15301&searchGroup=lecture) The 12th Joint Conference on Lexical and Computational Semantics (SEM 2023) is organized and sponsored by SIGLEX, the Special Interest Group of the ACL. SEM brings together researchers interested in the semantics of natural languages and its computational modeling. The conference embraces data-driven, neural, and probabilistic approaches, as well as symbolic approaches and everything in between; practical applications as well as theoretical contributions are welcome. The long-term goal of SEM is to provide a stable forum for the growing number of NLP researchers working on all aspects of semantics of (many and diverse!) natural languages. For more details visit our workshop website: https://sites.google.com/view/starsem2023
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W2: The 12th Joint Conference on Lexical and Computational Semantics (*SEM)_Day 1

workshop paper

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Machine Learning for NLP

technical paper

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
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**Session Chair: ** Marzena Karpinska

13C-Oral: Semantics: Lexical Semantics

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Mehdi Rezaee

3

SHORT BIO

Presentations

Semantically-informed Hierarchical Event Modeling

RevUp: Revise and Update Information Bottleneck for Event Representation

Event Representation with Sequential, Semi-Supervised Discrete Variables

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