  Hierarchical topic modeling aims to discover latent topics from a corpus and
organize them into a hierarchy to understand documents with desirable semantic
granularity. However, existing work struggles with producing topic hierarchies
of low affinity, rationality, and diversity, which hampers document
understanding. To overcome these challenges, we in this paper propose Transport
Plan and Context-aware Hierarchical Topic Model (TraCo). Instead of early
simple topic dependencies, we propose a transport plan dependency method. It
constrains dependencies to ensure their sparsity and balance, and also
regularizes topic hierarchy building with them. This improves affinity and
diversity of hierarchies. We further propose a context-aware disentangled
decoder. Rather than previously entangled decoding, it distributes different
semantic granularity to topics at different levels by disentangled decoding.
This facilitates the rationality of hierarchies. Experiments on benchmark
datasets demonstrate that our method surpasses state-of-the-art baselines,
effectively improving the affinity, rationality, and diversity of hierarchical
topic modeling with better performance on downstream tasks.
