Topic modeling with a tree-based prior has been used for a variety of applications because it can encode correlations between words that traditional topic modeling cannot. However, its expressive power comes at the cost of more complicated inference. We extend the S PARSE LDA (Yao et al., 2009) inference scheme for latent Dirichlet allocation (LDA) to tree-based topic models.