  Topic models are some of the most popular ways to represent textual data in
an interpret-able manner. Recently, advances in deep generative models,
specifically auto-encoding variational Bayes (AEVB), have led to the
introduction of unsupervised neural topic models, which leverage deep
generative models as opposed to traditional statistics-based topic models. We
extend upon these neural topic models by introducing the Label-Indexed Neural
Topic Model (LI-NTM), which is, to the extent of our knowledge, the first
effective upstream semi-supervised neural topic model. We find that LI-NTM
outperforms existing neural topic models in document reconstruction benchmarks,
with the most notable results in low labeled data regimes and for data-sets
with informative labels; furthermore, our jointly learned classifier
outperforms baseline classifiers in ablation studies.
