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arXiv · 1910.08772

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

Hai Hu·Qi Chen·Kyle Richardson·Atreyee Mukherjee·Lawrence S. Moss·Sandra Kuebler
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Abstract

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.

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Hai Hu, Qi Chen, Kyle Richardson, Atreyee Mukherjee, Lawrence S. Moss, Sandra Kuebler. 2019-10-19. MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity. https://arxiv.org/abs/1910.08772

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