  Topic models have been evolving rapidly over the years, from conventional to
recent neural models. However, existing topic models generally struggle with
either effectiveness, efficiency, or stability, highly impeding their practical
applications. In this paper, we propose FASTopic, a fast, adaptive, stable, and
transferable topic model. FASTopic follows a new paradigm: Dual
Semantic-relation Reconstruction (DSR). Instead of previous conventional,
neural VAE-based or clustering-based methods, DSR discovers latent topics by
reconstruction through modeling the semantic relations among document, topic,
and word embeddings. This brings about a neat and efficient topic modeling
framework. We further propose a novel Embedding Transport Plan (ETP) method.
Rather than early straightforward approaches, ETP explicitly regularizes the
semantic relations as optimal transport plans. This addresses the relation bias
issue and thus leads to effective topic modeling. Extensive experiments on
benchmark datasets demonstrate that our FASTopic shows superior effectiveness,
efficiency, adaptivity, stability, and transferability, compared to
state-of-the-art baselines across various scenarios. Our code is available at
https://github.com/bobxwu/FASTopic .
