发现功能词的统计特性可帮助语言学习，三特性在186种语言中普遍存在。
Function Words as Statistical Cues for Language Learning
- 分析186种语言的功能词分布，验证三类统计特性普遍存在。
- 神经模型实验显示功能词频率适中时学习效果最佳，呈黄金法则效应。
- 不同学习条件下对功能词的依赖程度系统性变化，适合语言认知研究者参考。
什么统计特性能支持从线性输入中习得抽象语法知识？我们通过考察功能词的统计分布来回答此问题。已有观点认为功能词可通过高频出现、可靠的句法关联和短语边界对齐三个分布特性促进习得。我们对186种语言进行了跨语言语料库分析，证实这三个特性具有普遍性。基于英语的反事实语言建模与消融实验表明，保留这些特性有助于神经学习器的习得效果，且存在‘恰到好处’效应：功能词需足够频繁以保证可靠性，又需足够多样以维持结构依赖信息量。探测分析进一步揭示，在不同学习条件下，对功能词的依赖程度呈现系统性差异。
原文摘要 · Abstract (English)
What statistical properties might support learning abstract grammatical knowledge from linear input? We address this question by examining the statistical distribution of function words. Function words have been argued to aid acquisition through three distributional properties: high frequency, reliable syntactic association, and phrase-boundary alignment. We conduct a cross-linguistic corpus analysis of 186 languages, which confirms that all three properties are universal. Using counterfactual language modeling and ablation experiments on English, we show that preserving these properties facilitates acquisition in neural learners, with a Goldilocks effect: function words must be frequent enough to be reliable, yet diverse enough to remain informative to structural dependency. Probing analyses further reveal that different learning conditions produce systematically different reliance on function words.
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