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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.08476 |
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| _version_ | 1866910203303165952 |
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| author | Marcheva, Mila Salhan, Suchir Sun, Weiwei |
| author_facet | Marcheva, Mila Salhan, Suchir Sun, Weiwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08476 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Computational Operationalisation of Competing Maturational Theories of Syntactic Development via Statistical Grammar Induction Marcheva, Mila Salhan, Suchir Sun, Weiwei Computation and Language 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. |
| title | A Computational Operationalisation of Competing Maturational Theories of Syntactic Development via Statistical Grammar Induction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.08476 |