将一阶逻辑自动转为自然语句，支持复杂嵌套结构。
FOL2NS: Generating Natural Sentences from First-Order Logic
- 结合规则模块与微调语言模型生成逻辑语句
- 能生成结构复杂的语法正确句子，但复杂度升高时语义精度下降
- 适合需要逻辑转自然语言的智能系统开发者
将形式化语言转换为自然语言是NLP的基础挑战，推动了语义解析、定理验证和问答等下游应用的发展。本文提出一阶逻辑到自然语句（FOL2NS）框架，用于生成合成的一阶逻辑公式，并将其转换为自然人类表达。该框架可处理具有不同量词深度（QD）的深层嵌套结构，而这类结构在现有语料库中罕见。通过结合规则驱动模块与微调语言模型，FOL2NS提升了生成样本的多样性与覆盖范围。实验中，我们通过字符级分析与整体性能指标系统评估框架能力。结果表明，FOL2NS能够可靠生成语法正确的模板和流畅语句，但在结构复杂度增加时面临语义表征精确性与自然生成的挑战。
原文摘要 · Abstract (English)
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.
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