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.

为了利用有可能是嘈杂或缺失的边缘信息，我们提出了一种基于半监督信息瓶颈离散隐变量模型。我们使用辅助连续隐变量和轻量级分层结构重新参数化模型的离散变量，以最小化已观测数据和未被新辅助变量捕获的可选边缘知识之间的互信息，并在事件建模上进行经验研究，并与强实验结果协作我们的理论结果。