  This paper analyzes negation in eight popular corpora spanning six natural
language understanding tasks. We show that these corpora have few negations
compared to general-purpose English, and that the few negations in them are
often unimportant. Indeed, one can often ignore negations and still make the
right predictions. Additionally, experimental results show that
state-of-the-art transformers trained with these corpora obtain substantially
worse results with instances that contain negation, especially if the negations
are important. We conclude that new corpora accounting for negation are needed
to solve natural language understanding tasks when negation is present.
