用统计语法归纳模拟儿童语法发展，发现词汇结构先于语篇结构出现。
A Computational Operationalisation of Competing Maturational Theories of Syntactic Development via Statistical Grammar Induction
- 通过统计语法归纳实现两种发育理论的计算模拟
- 底层理论在三项指标上显著优于内部理论
- 适合语言习得与认知建模研究者阅读
本文探讨儿童第一语言发展中所经历的中间句法类别及其顺序。成熟理论提出不同预测：自下而上模型（GROWING）认为词法和屈折结构先出现，而向内模型（INWARD）则预测早期即可获得语篇相关类别。本文通过统计语法归纳，将这两种假设转化为可计算的阶段性句法涌现框架，在保持输入和学习算法一致的前提下，考察不同成熟顺序对可学性的影响。该框架使类别习得过程显式化，揭示了在相同条件下不同成熟顺序如何塑造可学习的句法结构。基于此，GROWING 模型在三个评估指标上均显著优于 INWARD 模型。
原文摘要 · Abstract (English)
This paper is concerned with what intermediate syntactic categories children acquire during first language development, and in what order. Maturational theories make different predictions. Bottom-up accounts (GROWING) propose that lexical and inflectional structure emerges first, while inward accounts (INWARD) predict early access to discourse-related categories. We computationally operationalise these hypotheses of staged syntactic emergence using statistical grammar induction, asking what each proposed ordering makes learnable when input and learning algorithm are held constant. Our framework makes category acquisition explicit and allows us to explore how different maturational orderings shape the structure that can be learned under identical conditions. Based on this operationalisation, the GROWING account significantly outperforms the INWARD account across three evaluation metrics.
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