  We propose a new model for relational VAE semi-supervision capable of
balancing disentanglement and low complexity modelling of relations with
different symbolic properties. We compare the relative benefits of
relation-decoder complexity and latent space structure on both inductive and
transductive transfer learning. Our results depict a complex picture where
enforcing structure on semi-supervised representations can greatly improve
zero-shot transductive transfer, but may be less favourable or even impact
negatively the capacity for inductive transfer.
