  Providing natural language processing systems with commonsense knowledge is a
critical challenge for achieving language understanding. Recently, commonsense
knowledge models have emerged as a suitable approach for hypothesizing
situation-relevant commonsense knowledge on-demand in natural language
applications. However, these systems are limited by the fixed set of relations
captured by schemas of the knowledge bases on which they're trained.
  To address this limitation, we investigate training commonsense knowledge
models in a few-shot setting with limited tuples per commonsense relation in
the graph. We perform five separate studies on different dimensions of few-shot
commonsense knowledge learning, providing a roadmap on best practices for
training these systems efficiently. Importantly, we find that human quality
ratings for knowledge produced from a few-shot trained system can achieve
performance within 6% of knowledge produced from fully supervised systems. This
few-shot performance enables coverage of a wide breadth of relations in future
commonsense systems.
