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Dropout is a widely used regularization trick to resolve the overfittingissue in large feedforward neural networks trained on a small dataset, whichperforms poorly on the held-out test subset. Although the effectiveness of thisregularization trick has been extensively studied for convolutional neuralnetworks, there is a lack of analysis of it for unsupervised models and inparticular, VAE-based neural topic models. In this paper, we have analyzed theconsequences of dropout in the encoder as well as in the decoder of the VAEarchitecture in three widely used neural topic models, namely, contextualizedtopic model (CTM), ProdLDA, and embedded topic model (ETM) using four publiclyavailable datasets. We characterize the dropout effect on these models in termsof the quality and predictive performance of the generated topics.

Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling