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

arXiv: https://arxiv.org/abs/1910.08772 · HTML: https://mlsanity.com/paper/1910.08772/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2019.

## Built on by (most cited first, 21 of 21)

- [Generating Natural Language Proofs with Verifier-Guided Search](https://mlsanity.com/paper/2205.12443/) (2022)
- [Natural Language Deduction through Search over Statement Compositions](https://mlsanity.com/paper/2201.06028/) (2022)
- [Flexible Generation of Natural Language Deductions](https://mlsanity.com/paper/2104.08825/) (2021)
- [NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning](https://mlsanity.com/paper/2105.14167/) (2021)
- [Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models](https://mlsanity.com/paper/2205.11432/) (2022)
- [RECKONING: Reasoning through Dynamic Knowledge Encoding](https://mlsanity.com/paper/2305.06349/) (2023)
- [Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic](https://mlsanity.com/paper/2109.08927/) (2021)
- [Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference](https://mlsanity.com/paper/2203.04857/) (2022)
- [Probing Linguistic Information For Logical Inference In Pre-trained Language Models](https://mlsanity.com/paper/2112.01753/) (2021)
- [Supporting Context Monotonicity Abstractions in Neural NLI Models](https://mlsanity.com/paper/2105.08008/) (2021)
- [Decomposing Natural Logic Inferences in Neural NLI](https://mlsanity.com/paper/2112.08289/) (2021)
- [Monotonicity Marking from Universal Dependency Trees](https://mlsanity.com/paper/2104.08659/) (2021)
- [NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic](https://mlsanity.com/paper/2307.02849/) (2023)
- [Identification of Entailment and Contradiction Relations for Natural Language Argumentation: A Neurosymbolic Approach](https://mlsanity.com/paper/2405.01259/) (2024)
- [Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference](https://mlsanity.com/paper/2508.14735/) (2025)
- [Logical Inference for Counting on Semi-structured Tables](https://mlsanity.com/paper/2204.07803/) (2022)
- [Synthetic Dataset for Evaluating Complex Compositional Knowledge for Natural Language Inference](https://mlsanity.com/paper/2307.05034/) (2023)
- [Implementing a Logical Inference System for Japanese Comparatives](https://mlsanity.com/paper/2509.13734/) (2025)
- [Quantum NLP models on Natural Language Inference](https://mlsanity.com/paper/2510.15972/) (2025)
- [Linguistic Features for Interpretable Textual Entailment](https://mlsanity.com/paper/2609.24932/) (2026)
- [Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI](https://mlsanity.com/paper/2610.15158/) (2026)

## Abstract (arXiv)

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.