  Generative classifiers offer potential advantages over their discriminative
counterparts, namely in the areas of data efficiency, robustness to data shift
and adversarial examples, and zero-shot learning (Ng and Jordan,2002; Yogatama
et al., 2017; Lewis and Fan,2019). In this paper, we improve generative text
classifiers by introducing discrete latent variables into the generative story,
and explore several graphical model configurations. We parameterize the
distributions using standard neural architectures used in conditional language
modeling and perform learning by directly maximizing the log marginal
likelihood via gradient-based optimization, which avoids the need to do
expectation-maximization. We empirically characterize the performance of our
models on six text classification datasets. The choice of where to include the
latent variable has a significant impact on performance, with the strongest
results obtained when using the latent variable as an auxiliary conditioning
variable in the generation of the textual input. This model consistently
outperforms both the generative and discriminative classifiers in small-data
settings. We analyze our model by using it for controlled generation, finding
that the latent variable captures interpretable properties of the data, even
with very small training sets.
