One of the key discoveries of this paper is that adding noise to the document-topic vectors during training leads to substantially better topics.
for each training example we sample a random Dirichlet vector from the prior θ noise ∼ P Θ and mix with the encoder output θ = enc(w): where α ∈ [0, 1] is the mixing proportion.