  Embedded topic models are able to learn interpretable topics even with large
and heavy-tailed vocabularies. However, they generally hold the Euclidean
embedding space assumption, leading to a basic limitation in capturing
hierarchical relations. To this end, we present a novel framework that
introduces hyperbolic embeddings to represent words and topics. With the
tree-likeness property of hyperbolic space, the underlying semantic hierarchy
among words and topics can be better exploited to mine more interpretable
topics. Furthermore, due to the superiority of hyperbolic geometry in
representing hierarchical data, tree-structure knowledge can also be naturally
injected to guide the learning of a topic hierarchy. Therefore, we further
develop a regularization term based on the idea of contrastive learning to
inject prior structural knowledge efficiently. Experiments on both topic
taxonomy discovery and document representation demonstrate that the proposed
framework achieves improved performance against existing embedded topic models.
