  Statistical morphological inflectors are typically trained on fully
supervised, type-level data. One remaining open research question is the
following: How can we effectively exploit raw, token-level data to improve
their performance? To this end, we introduce a novel generative latent-variable
model for the semi-supervised learning of inflection generation. To enable
posterior inference over the latent variables, we derive an efficient
variational inference procedure based on the wake-sleep algorithm. We
experiment on 23 languages, using the Universal Dependencies corpora in a
simulated low-resource setting, and find improvements of over 10% absolute
accuracy in some cases.
