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Improving Neural Topic Models using Knowledge Distillation
[article]
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
arXiv:2010.02377v1
fatcat:xm7rr7hw7nc6nngheo4x5t6ncu