  Negation poses a challenge in many natural language understanding tasks.
Inspired by the fact that understanding a negated statement often requires
humans to infer affirmative interpretations, in this paper we show that doing
so benefits models for three natural language understanding tasks. We present
an automated procedure to collect pairs of sentences with negation and their
affirmative interpretations, resulting in over 150,000 pairs. Experimental
results show that leveraging these pairs helps (a) T5 generate affirmative
interpretations from negations in a previous benchmark, and (b) a RoBERTa-based
classifier solve the task of natural language inference. We also leverage our
pairs to build a plug-and-play neural generator that given a negated statement
generates an affirmative interpretation. Then, we incorporate the pretrained
generator into a RoBERTa-based classifier for sentiment analysis and show that
doing so improves the results. Crucially, our proposal does not require any
manual effort.
