  Pretrained language models have excelled at many NLP tasks recently; however,
their social intelligence is still unsatisfactory. To enable this, machines
need to have a more general understanding of our complicated world and develop
the ability to perform commonsense reasoning besides fitting the specific
downstream tasks. External commonsense knowledge graphs (KGs), such as
ConceptNet, provide rich information about words and their relationships. Thus,
towards general commonsense learning, we propose two approaches to
\emph{implicitly} and \emph{explicitly} infuse such KGs into pretrained
language models. We demonstrate our proposed methods perform well on SocialIQA,
a social commonsense reasoning task, in both limited and full training data
regimes.
