[Submitted on 15 Oct 2026]
Title:From Parallels to Divergence: Language Models' Learning of Action Nominal Constructions
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Abstract:Action nominal constructions (ANCs) express events in nominal form, with their argument marking following patterns found in clauses or ordinary noun phrases. In this paper, we investigate how different grammatical configurations affect language models' learning of ANCs and related constructions, and how these learning preferences compare with typological distributions in natural languages. We use a grammar-engineered method for synthetic corpus construction that combines shared semantic input from natural corpora with computational grammars. We construct 96 target languages varying in clause word order, noun-phrase word order, alignment, complement system, and ANC strategy. For each language, we train GPT-2-small models from scratch and evaluate them using targeted minimal pairs covering 34 grammatical phenomena. The results show typological parallels in basic clause configurations but divergences in complement systems and ANC strategies, with models also learning combinations rare or unattested in natural languages. This partial alignment suggests that natural language distributions may reflect both general learning pressures and functional demands not fully captured by model learning experiments.
Comments: 41 pages, including appendices
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2610.22468 [cs.CL]
(or arXiv:2610.22468v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.22468
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Iskar Deng [view email] [v1] Thu, 15 Oct 2026 17:43:47 UTC (566 KB)
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