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
this https URL .

FASTopic 是一种快速、自适应、稳定和可传递的主题模型，采用 Dual Semantic-relation Reconstruction（DSR）方法来发现潜在主题，并通过模拟文档、主题和词嵌入之间的语义关系进行重建，以提供一个高效的主题建模框架。同时，还提出了一种新颖的 Embedding Transport Plan（ETP）方法来解决关系偏差问题，通过优化传输方案中的语义关系，从而实现更有效的主题建模。大量的实验结果表明，相比各种情景下的现有基准模型，FASTopic 在有效性、效率、适应性、稳定性和可传递性方面都表现出优势。