FOL2NS: Generating Natural Sentences from First-Order Logic
For researchers in semantic parsing and theorem validation, FOL2NS provides a method to generate training data with controlled logical complexity, though its limitations in semantic accuracy are acknowledged.
FOL2NS introduces a neurosymbolic framework to generate synthetic first-order logic formulas and convert them into natural sentences, addressing the lack of deeply nested structures in existing corpora. Experiments show it produces well-formed templates and fluent statements but struggles with precise semantics and naturalness as complexity increases.
Translating formal language into natural language is a foundational challenge in NLP, driving various downstream applications in semantic parsing, theorem validation, and question answering. In this study, we introduce First-Order Logic to Natural Sentence (FOL2NS), a neurosymbolic framework designed to generate synthetic FOL formulas and convert them into natural human expressions. It handles deeply nested structures with varying quantifier depths (QD), which are rarely captured by existing corpora. By combining rule-driven modules with fine-tuned language models, FOL2NS enhances the diversity and coverage of the generated samples. In our experiments, we systematically evaluate the framework's capabilities through both character-level analysis and overall performance metrics. Experimental results show that FOL2NS can reliably produce well-formed templates and fluent statements, but it faces challenges in achieving precise semantic representations and natural generation as structural complexity increases.