Probabilistic Topic Modeling With Transformer Representations.

Abstract

The field of topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in natural language processing, however, several successful models that rely on straightforward clustering approaches in transformer-based embedding spaces have emerged and consolidated the notion of topics as clusters of embedding vectors. We propose the transformer-representation neural topic model (TNTM), which combines the benefits of topic representations in transformer-based embedding spaces and probabilistic modeling. Therefore, this approach unifies the powerful and versatile notion of topics based on transformer embeddings with fully probabilistic modeling, as in models such as latent Dirichlet allocation (LDA). We utilize the variational autoencoder (VAE) framework for improved inference speed and modeling flexibility. Experimental results show that our proposed model achieves results on par with various state-of-the-art approaches in terms of embedding coherence while maintaining almost perfect topic diversity. The corresponding source code is available at: https://github.com/ArikReuter/TNTM.