  Relational Networks (RN) as introduced by Santoro et al. (2017) have
demonstrated strong relational reasoning capabilities with a rather shallow
architecture. Its single-layer design, however, only considers pairs of
information objects, making it unsuitable for problems requiring reasoning
across a higher number of facts. To overcome this limitation, we propose a
multi-layer relation network architecture which enables successive refinements
of relational information through multiple layers. We show that the increased
depth allows for more complex relational reasoning by applying it to the bAbI
20 QA dataset, solving all 20 tasks with joint training and surpassing the
state-of-the-art results.
