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.

本文提出了一种基于传输计划和上下文感知的层次主题模型（TraCo），通过传输计划依赖方法和上下文感知的解码器来改善层次主题建模的关联性、合理性和多样性，在基准数据集上的实验证明了该方法的优越性。

层次主题建模的关联性、合理性和多样性

On the Affinity, Rationality, and Diversity of Hierarchical Topic  Modeling

Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies
in a collection of documents. However, traditional HTMs often produce
hierarchies where lowerlevel topics are unrelated and not specific enough to
their higher-level topics. Additionally, these methods can be computationally
expensive. We present HyHTM - a Hyperbolic geometry based Hierarchical Topic
Models - that addresses these limitations by incorporating hierarchical
information from hyperbolic geometry to explicitly model hierarchies in topic
models. Experimental results with four baselines show that HyHTM can better
attend to parent-child relationships among topics. HyHTM produces coherent
topic hierarchies that specialise in granularity from generic higher-level
topics to specific lowerlevel topics. Further, our model is significantly
faster and leaves a much smaller memory footprint than our best-performing
baseline.We have made the source code for our algorithm publicly accessible.

使用基于双曲几何的层级主题模型（HyHTM）可以更好地对主题进行建模，使得特定的低级主题与相应的高级主题关联更紧密。同时，该模型也具备更高的计算效率和更小的内存占用。