  We present the first comprehensive study on automatic knowledge base
construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et
al., 2019) and ConceptNet (Speer et al., 2017). Contrary to many conventional
KBs that store knowledge with canonical templates, commonsense KBs only store
loosely structured open-text descriptions of knowledge. We posit that an
important step toward automatic commonsense completion is the development of
generative models of commonsense knowledge, and propose COMmonsEnse
Transformers (COMET) that learn to generate rich and diverse commonsense
descriptions in natural language. Despite the challenges of commonsense
modeling, our investigation reveals promising results when implicit knowledge
from deep pre-trained language models is transferred to generate explicit
knowledge in commonsense knowledge graphs. Empirical results demonstrate that
COMET is able to generate novel knowledge that humans rate as high quality,
with up to 77.5% (ATOMIC) and 91.7% (ConceptNet) precision at top 1, which
approaches human performance for these resources. Our findings suggest that
using generative commonsense models for automatic commonsense KB completion
could soon be a plausible alternative to extractive methods.
