  Building on Petroni et al. (2019), we propose two new probing tasks analyzing
factual knowledge stored in Pretrained Language Models (PLMs). (1) Negation. We
find that PLMs do not distinguish between negated ("Birds cannot [MASK]") and
non-negated ("Birds can [MASK]") cloze questions. (2) Mispriming. Inspired by
priming methods in human psychology, we add "misprimes" to cloze questions
("Talk? Birds can [MASK]"). We find that PLMs are easily distracted by
misprimes. These results suggest that PLMs still have a long way to go to
adequately learn human-like factual knowledge.
