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

MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity