  Recent years have brought about a renewed interest in commonsense
representation and reasoning in the field of natural language understanding.
The development of new commonsense knowledge graphs (CSKG) has been central to
these advances as their diverse facts can be used and referenced by machine
learning models for tackling new and challenging tasks. At the same time, there
remain questions about the quality and coverage of these resources due to the
massive scale required to comprehensively encompass general commonsense
knowledge.
  In this work, we posit that manually constructed CSKGs will never achieve the
coverage necessary to be applicable in all situations encountered by NLP
agents. Therefore, we propose a new evaluation framework for testing the
utility of KGs based on how effectively implicit knowledge representations can
be learned from them.
  With this new goal, we propose ATOMIC 2020, a new CSKG of general-purpose
commonsense knowledge containing knowledge that is not readily available in
pretrained language models. We evaluate its properties in comparison with other
leading CSKGs, performing the first large-scale pairwise study of commonsense
knowledge resources. Next, we show that ATOMIC 2020 is better suited for
training knowledge models that can generate accurate, representative knowledge
for new, unseen entities and events. Finally, through human evaluation, we show
that the few-shot performance of GPT-3 (175B parameters), while impressive,
remains ~12 absolute points lower than a BART-based knowledge model trained on
ATOMIC 2020 despite using over 430x fewer parameters.
