  We propose a multi-task, probabilistic approach to facilitate distantly
supervised relation extraction by bringing closer the representations of
sentences that contain the same Knowledge Base pairs. To achieve this, we bias
the latent space of sentences via a Variational Autoencoder (VAE) that is
trained jointly with a relation classifier. The latent code guides the pair
representations and influences sentence reconstruction. Experimental results on
two datasets created via distant supervision indicate that multi-task learning
results in performance benefits. Additional exploration of employing Knowledge
Base priors into the VAE reveals that the sentence space can be shifted towards
that of the Knowledge Base, offering interpretability and further improving
results.
