Improving Neural Topic Models using Knowledge Distillation [article]

Alexander Hoyle, Pranav Goel, Philip Resnik
2020 arXiv   pre-print
Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular method can be straightforwardly applied with any neural topic model to improve topic quality, which we demonstrate using two models having disparate architectures, obtaining state-of-the-art topic coherence. We show that our adaptable framework not
more » ... ly improves performance in the aggregate over all estimated topics, as is commonly reported, but also in head-to-head comparisons of aligned topics.
arXiv:2010.02377v1 fatcat:xm7rr7hw7nc6nngheo4x5t6ncu