  Combinatorial generalization - the ability to understand and produce novel
combinations of already familiar elements - is considered to be a core capacity
of the human mind and a major challenge to neural network models. A significant
body of research suggests that conventional neural networks can't solve this
problem unless they are endowed with mechanisms specifically engineered for the
purpose of representing symbols. In this paper we introduce a novel way of
representing symbolic structures in connectionist terms - the vectors approach
to representing symbols (VARS), which allows training standard neural
architectures to encode symbolic knowledge explicitly at their output layers.
In two simulations, we show that neural networks not only can learn to produce
VARS representations, but in doing so they achieve combinatorial generalization
in their symbolic and non-symbolic output. This adds to other recent work that
has shown improved combinatorial generalization under specific training
conditions, and raises the question of whether specific mechanisms or training
routines are needed to support symbolic processing.
