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The existence of external (``side'') semantic knowledge has been shown toresult in more expressive computational event models. To enable the use of sideinformation that may be noisy or missing, we propose a semi-supervisedinformation bottleneck-based discrete latent variable model. We reparameterizethe model's discrete variables with auxiliary continuous latent variables and alight-weight hierarchical structure. Our model is learned to minimize themutual information between the observed data and optional side knowledge thatis not already captured by the new, auxiliary variables. We theoretically showthat our approach generalizes past approaches, and perform an empirical casestudy of our approach on event modeling. We corroborate our theoretical resultswith strong empirical experiments, showing that the proposed method outperformsprevious proposed approaches on multiple datasets.

RevUp: Revise and Update Information Bottleneck for Event Representation