Paper
Authors: Hai Hu (Indiana University Bloomington) , Qi Chen (Indiana University Bloomington) , Kyle Richardson (Allen Institute for Artificial Intelligence) , Atreyee Mukherjee (Indiana University Bloomington) , Lawrence S Moss (Indiana University Bloomington) , Sandra Kübler (Indiana University Bloomington)
We present a new logic-based inference engine for natural language inference called MonaLog, which is based on the monotonicity calculus and natural logic. In contrast to existing logic-based approaches, our system is lightweight, and operates using a small set of well-known monotonicity facts about quantifiers and lexical items. Despite its simplicity, we find it competitive with other logic-based NLI models on the SICK benchmark. We also use MonaLog incombination with BERT in a variety of settings, including data augmentation. We show that MonaLog is capable of generating large amounts of high-quality training data for BERT, improving its accuracy on SICK.
Keywords: natural language inference, monotonicity, natural logic, BERT, data augmentation
How to Cite: Hu, H. , Chen, Q. , Richardson, K. , Mukherjee, A. , Moss, L. S. & Kübler, S. (2020) “MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity”, Society for Computation in Linguistics. 3(1). doi: https://doi.org/10.7275/te06-g113