Evolving language compositionality in a frequency-structured meaning space
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Computer Science > Computation and Language
Title:Evolving language compositionality in a frequency-structured meaning space
Abstract:The iterated learning model was introduced to investigate language evolution: the way in which the characteristic properties of human languages have been shaped, at least partly, by repeated transmission from one language user to another. The key finding is that language compositionality can arise spontaneously as a consequence of language being passed repeatedly through a language learning bottleneck. Here we explore how changing the frequency of different meanings, so that some meanings occur much more frequently than others, affects the character of its compositionality. We find that, as observed in natural languages, high-frequency meanings can escape the pressure to conform to the grammar that characterizes lower-frequency meanings. However, when the frequency structure is instead imposed on parts rather than on whole meaning vectors, the language fails to transmit across generations. This occurs despite the fact that the most frequent elements are reliably learned. These results suggest that frequency can shape emergent linguistic structure only when the frequency distribution is defined over form-meaning units that learners can acquire holistically. When frequency is instead distributed over smaller units, it fails to support the relational structure required for compositional generalisation, thereby preventing stable language transmission.
| Comments: | 17 pages, 4 figures (plus 2 figures in appendix), submitted to Wivace 2026 (this https URL) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.29642 [cs.CL] |
| (or arXiv:2607.29642v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.29642
arXiv-issued DOI via DataCite (pending registration)
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