  To apply eyeshadow without a brush, should I use a cotton swab or a
toothpick? Questions requiring this kind of physical commonsense pose a
challenge to today's natural language understanding systems. While recent
pretrained models (such as BERT) have made progress on question answering over
more abstract domains - such as news articles and encyclopedia entries, where
text is plentiful - in more physical domains, text is inherently limited due to
reporting bias. Can AI systems learn to reliably answer physical common-sense
questions without experiencing the physical world? In this paper, we introduce
the task of physical commonsense reasoning and a corresponding benchmark
dataset Physical Interaction: Question Answering or PIQA. Though humans find
the dataset easy (95% accuracy), large pretrained models struggle (77%). We
provide analysis about the dimensions of knowledge that existing models lack,
which offers significant opportunities for future research.
