  Transformer models pre-trained with a masked-language-modeling objective
(e.g., BERT) encode commonsense knowledge as evidenced by behavioral probes;
however, the extent to which this knowledge is acquired by systematic inference
over the semantics of the pre-training corpora is an open question. To answer
this question, we selectively inject verbalized knowledge into the minibatches
of a BERT model during pre-training and evaluate how well the model generalizes
to supported inferences. We find generalization does not improve over the
course of pre-training, suggesting that commonsense knowledge is acquired from
surface-level, co-occurrence patterns rather than induced, systematic
reasoning.
