1 code implementation • 20 Dec 2022 • Shubhashis Roy Dipta, Mehdi Rezaee, Francis Ferraro
Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches.
1 code implementation • 24 May 2022 • Mehdi Rezaee, Francis Ferraro
We reparameterize the model's discrete variables with auxiliary continuous latent variables and a light-weight hierarchical structure.
no code implementations • 15 Sep 2021 • Mehdi Rezaee, Kasra Darvish, Gaoussou Youssouf Kebe, Francis Ferraro
We re-examine the situation entity (SE) classification task with varying amounts of available training data.
1 code implementation • NeurIPS 2020 • Mehdi Rezaee, Francis Ferraro
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.
no code implementations • NAACL 2021 • Mehdi Rezaee, Francis Ferraro
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.