Neural Language Models Learn the Contextual Distributions of Dependency Structures: a statistical learning theory to compositionality
It is unclear how Neural Language Models (NLMs) acquire the structural meaning encoded by grammatical structures that is independent of lexical semantics. We propose a statistical learning process in which learned dependency structures themselves become new distributional units for subsequent statistical learning. Under this account, once a dependency structure is acquired, the model tracks its contextual distributions. These contextual features reflect the semantic properties of a composite structure. To test this hypothesis, we design a synthetic grammar in which each grammatical structure has distinct contextual distributions that cannot be recovered from the distributional statistics ...
| Authors | Wang Bojun, Junjie Chen, Holly Jenkins et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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