发表机构
Guangdong Haiqixing Marine Technology Co., Ltd.（广东海启星海洋科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过将QuanLing框架扩展到西罗曼语支（法、葡、西、意），验证了其跨分支适用性，发现葡西最接近、法意最远，且与北日耳曼语结果一致，证明其泛化能力。
AI 中文摘要
量化密切相关的语言之间的语言距离仍然是定量语言学中的核心挑战。我们之前的工作[1]提出了QuanLing（基于预训练语言模型的定量语言学），这是一个结合语言距离度量（句子嵌入距离、分词碎片率）与语言属性分析（MLM预测概率）的定量框架，并在北日耳曼语支（丹麦语、挪威博克马尔语、瑞典语）上进行了验证。本文将QuanLing扩展到西罗曼语支——法语、葡萄牙语、西班牙语、意大利语——使用与北日耳曼语研究相同的度量族和聚合协议测试跨分支适用性，并针对四种语言进行了调整（英语锚点、四元组构建）。使用150个四种语言的平行句子，我们计算了LaBSE句子嵌入距离、来自四个单语BERT分词器的分词碎片率，以及mBERT掩码语言模型的相互可理解性。结果表明，葡萄牙语与西班牙语最为接近（LaBSE距离0.0229），法语与意大利语最远（0.0338）；LaBSE和mBERT的排名在6对中有4对一致，确认了跨模型的稳健性。西罗曼语支的绝对距离跨度比北日耳曼语支更宽（0.011对0.008），但相对比率相当（1.48对1.67），与更长的分化时间一致。法语表现出显著更高的MLM可预测性（top-1准确率36.12%对意大利语的29.28%），反映了其正字法-音系解耦。这一跨分支验证为QuanLing在单一语言分支之外的泛化能力提供了进一步证据。
英文摘要
Quantifying language distance among closely related languages remains a core challenge in quantitative linguistics. Our previous work [1] introduced QuanLing (Quantitative Linguistics via Pretrained Language Models), a quantitative framework combining language distance metrics (sentence embedding distance, tokenization fragmentation rate) with language property analysis (MLM prediction probability), validated on North Germanic (Danish, Norwegian Bokmål, Swedish). This paper extends QuanLing to Western Romance--French, Portuguese, Spanish, Italian--testing cross-branch applicability with the same metric family and aggregation protocol as our North Germanic study, adapted for four languages (English anchor, quadruplet construction). Using 150 four-language parallel sentences, we compute LaBSE sentence embedding distances, tokenization fragmentation rates from four monolingual BERT tokenizers, and mBERT masked language model mutual intelligibility. Results show that Portuguese--Spanish are closest (LaBSE distance 0.0229), French--Italian most distant (0.0338); LaBSE and mBERT rankings agree on 4 of 6 pairs, confirming cross-model robustness. Western Romance shows a wider absolute distance span than North Germanic (0.011 vs. 0.008) but comparable relative ratios (1.48 vs. 1.67), consistent with longer divergence time. French exhibits notably higher MLM predictability (36.12% top-1 accuracy vs. 29.28% for Italian), reflecting its orthography--phonology decoupling. This cross-branch validation provides further evidence for QuanLing's generalizability beyond a single language branch.
