  BERT has been used for solving commonsense tasks such as CommonsenseQA. While
prior research has found that BERT does contain commonsense information to some
extent, there has been work showing that pre-trained models can rely on
spurious associations (e.g., data bias) rather than key cues in solving
sentiment classification and other problems. We quantitatively investigate the
presence of structural commonsense cues in BERT when solving commonsense tasks,
and the importance of such cues for the model prediction. Using two different
measures, we find that BERT does use relevant knowledge for solving the task,
and the presence of commonsense knowledge is positively correlated to the model
accuracy.
