FOL2NS: Generating Natural Sentences from First-Order Logic

2026-05-18 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A new neurosymbolic framework, First-Order Logic to Natural Sentence (FOL2NS), has been developed to translate formal first-order logic (FOL) formulas into natural language sentences. This framework generates synthetic FOL formulas, including those with deeply nested structures and varying quantifier depths (QD), which are often absent in current datasets. FOL2NS integrates rule-driven modules with fine-tuned language models to improve the diversity and coverage of its generated samples. Experimental evaluations, including character-level analysis, demonstrate that FOL2NS consistently produces well-formed templates and fluent statements. However, the framework encounters difficulties in maintaining precise semantic representations and natural generation quality as the structural complexity of the FOL formulas increases.

Key takeaway

For research scientists working on semantic parsing or question answering, FOL2NS offers a method to generate diverse, complex FOL-to-natural language data. You should consider integrating this neurosymbolic approach to augment existing corpora, especially for deeply nested logical structures. Be aware that semantic precision and naturalness may degrade with extreme structural complexity.

Key insights

FOL2NS is a neurosymbolic framework translating complex first-order logic into natural language, addressing data scarcity.

Principles

• Neurosymbolic systems enhance diversity.
• Rule-driven modules improve sample coverage.

Method

FOL2NS combines rule-driven modules with fine-tuned language models to generate synthetic FOL formulas and convert them into natural language expressions, handling deeply nested structures and varying quantifier depths.

In practice

• Generate synthetic FOL formulas.
• Convert formal logic to natural text.

Topics

• FOL2NS
• First-Order Logic
• Natural Language Generation
• Neurosymbolic AI
• Semantic Parsing

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.