  We present a new logic-based inference engine for natural language inference
(NLI) called MonaLog, which is based on natural logic and the monotonicity
calculus. In contrast to existing logic-based approaches, our system is
intentionally designed to be as lightweight as possible, and operates using a
small set of well-known (surface-level) monotonicity facts about quantifiers,
lexical items and tokenlevel polarity information. Despite its simplicity, we
find our approach to be competitive with other logic-based NLI models on the
SICK benchmark. We also use MonaLog in combination with the current
state-of-the-art model BERT in a variety of settings, including for
compositional data augmentation. We show that MonaLog is capable of generating
large amounts of high-quality training data for BERT, improving its accuracy on
SICK.
