  Despite serving as the foundation models for a wide range of NLP benchmarks,
pre-trained language models have shown limited capabilities of acquiring
implicit commonsense knowledge from self-supervision alone, compared to
learning linguistic and factual knowledge that appear more explicitly in the
surface patterns in text. In this work, we introduce commonsense knowledge
transfer, a framework to transfer the commonsense knowledge stored in a neural
commonsense knowledge model to a general-purpose pre-trained language model. It
first exploits general texts to form queries for extracting commonsense
knowledge from the neural commonsense knowledge model and then refines the
language model with two self-supervised objectives: commonsense mask infilling
and commonsense relation prediction, which align human language with the
underlying commonsense knowledge. Empirical results show that our approach
consistently improves the model's performance on downstream tasks that require
commonsense reasoning. Moreover, we find that the improvement is more
significant in the few-shot setting. This suggests that our approach helps
language models better transfer to downstream tasks without extensive
supervision by injecting commonsense knowledge into their parameters.
