Papers by Mehdi Rezaee

3 papers
RevUp: Revise and Update Information Bottleneck for Event Representation (2023.eacl-main)

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Challenge: Existing external (“side”) semantic knowledge has been shown to result in more expressive computational event models.
Approach: They propose a semi-supervised information bottleneck-based discrete latent variable model that reparameterizes discrete variables with auxiliary continuous latent variables and a light-weight hierarchical structure.
Outcome: The proposed model outperforms existing models on multiple datasets.
Semantically-informed Hierarchical Event Modeling (2023.starsem-1)

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Challenge: Existing approaches to event modeling combine sequential latent variables with semantic ontological knowledge to improve representational capabilities.
Approach: They propose a doubly hierarchical semi-supervised event modeling framework that provides structural hierarchy while accounting for ontological hierarchy.
Outcome: The proposed model outperforms state-of-the-art models by 8.5% across two datasets and four metrics.
Event Representation with Sequential, Semi-Supervised Discrete Variables (2021.naacl-main)

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Challenge: Existing methods for event modeling take discrete, external knowledge into account . obtaining fully accurate structured knowledge can be difficult .
Approach: They propose a method that takes partially-observed sequences of discrete, external knowledge into account.
Outcome: The proposed method outperforms baselines and state-of-the-art in script induction and converges faster.

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