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Found 5 papers, 3 papers with code
Semantically-informed Hierarchical Event Modeling
Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches.
RevUp: Revise and Update Information Bottleneck for Event Representation
We reparameterize the model's discrete variables with auxiliary continuous latent variables and a light-weight hierarchical structure.
Discriminative and Generative Transformer-based Models For Situation Entity Classification
We re-examine the situation entity (SE) classification task with varying amounts of available training data.
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
We show how to learn a neural topic model with discrete random variables---one that explicitly models each word's assigned topic---using neural variational inference that does not rely on stochastic backpropagation to handle the discrete variables.
Event Representation with Sequential, Semi-Supervised Discrete Variables
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.
