科学领域的趋同，宗教领域的分歧：维基百科各语言版本间的校准框架差异

中文摘要 AI 辅助

当维基百科的各语言版本描述同一概念时，它们的表述框架会有多大差异？现有研究多衡量版本间的内容覆盖缺口，本研究则针对匹配的概念，测量其框架距离。我们分析了3000个潜在概念-语言观测值中的2799篇有效文章，涵盖150个由Wikidata锚定的概念、20种语言版本、4个领域及一个校准集。原始嵌入距离既反映内容差异，也体现编码器对各语言对的对齐程度。即便在具有跨文化稳定指称的校准概念（如化学元素、数字、颜色）中，最大语言对平均距离也是最小距离的3.6倍，且同语族内的距离通常更小。我们定义了经基线调整的距离（校准距离）：即某概念的两个语言版本间的距离，减去同语言对中校准概念的平均距离。该调整大幅降低了特定语言对的对齐差异及语族模式。在三种多语言编码器（LaBSE、multilingual MPNet、CMLM）中，科学类文章的对齐程度高于校准类文章，且三种编码器均将宗教领域的对齐程度排第一，科学技术领域排最后。概念级排名在不同编码器间高度一致（MPNet和CMLM相对于LaBSE的斯皮尔曼相关系数rho为0.75-0.79）。在LaBSE下，宗教领域的对齐程度显著高于校准基线。在政治领域内，分歧集中于审查制度、难民等概念，而民主、人权则是对齐程度最高的概念。本研究发布了代码、数据及各语言对的校准基线。

英文摘要

When Wikipedia's language editions describe the same concept, how differently do they frame it? Prior work measures coverage gaps between editions; we measure framing distance for matched concepts. We analyze 2,799 valid articles from 3,000 possible concept-language observations, spanning 150 Wikidata-anchored concepts, 20 language editions, 4 domains, and a calibration set. Raw embedding distances reflect both content differences and how well the encoder aligns each language pair. Even among calibration concepts with stable cross-cultural denotations (e.g., chemical elements, numbers, colors), the largest language-pair mean distance is 3.6 times the smallest, and distances are typically smaller within language families. We define a baseline-adjusted distance (calibrated distance): the distance between two language versions of a concept minus the mean distance for calibration concepts in the same language pair. This adjustment substantially reduces pair-specific alignment differences and the language-family pattern. Across three multilingual encoders (LaBSE, multilingual MPNet, and CMLM), scientific articles align more closely than calibration articles, and all three rank religion first and science/technology last. Concept-level rankings are highly consistent across encoders (Spearman rho=0.75-0.79 for MPNet and CMLM relative to LaBSE). Religion lies significantly above the calibration baseline under LaBSE. Within politics, divergence concentrates on concepts such as censorship and refugee, while democracy and human rights are among the most aligned. Code, data, and per-language-pair calibration baselines are released.\footnote{https://github.com/hhchen1105/cross-linqual-concept}

发表机构

• National Central University（中央大学）
• Computer Science and Information Engineering（计算机科学与信息工程）

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