论文标题
修订：修订和更新事件表示信息的信息瓶颈
RevUp: Revise and Update Information Bottleneck for Event Representation
论文作者
论文摘要
已经证明了外部（``'''）语义知识的存在可导致更具表现力的计算事件模型。为了启用可能嘈杂或丢失的侧面信息，我们提出了一个半监督信息的基于基于瓶颈的离散潜在变量模型。我们使用辅助连续变量和轻量重量层次结构对模型的离散变量进行重新聚集。学会了我们的模型，以最大程度地减少观察到的数据和可选侧面知识之间的相互信息，而新辅助变量尚未捕获的可选侧面知识。从理论上讲，我们表明我们的方法概括了过去的方法，并对我们在事件建模方面的方法进行经验案例研究。我们通过强大的经验实验证实了我们的理论结果，表明所提出的方法在多个数据集上优于先前提出的方法。
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.
