Finding Concept-specific Biases in Form--Meaning Associations

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This work presents an information-theoretic operationalisation of cross-linguistic non-arbitrariness. It is not a new idea that there are small, cross-linguistic associations between the forms and meanings of words. For instance, it has been claimed (Blasi et al., 2016) that the word for "tongue" is more likely than chance to contain the phone [l]. By controlling for the influence of language family and geographic proximity within a very large concept-aligned cross-lingual lexicon, we extend methods previously used to detect within language non-arbitrariness (Pimentel et al., 2019) to measure cross-linguistic associations. We find that there is a significant effect of non-arbitrariness, but it is unsurprisingly small (less than 0.5% on average according to our information-theoretic estimate). We also provide a concept-level analysis which shows that a quarter of the concepts considered in our work exhibit a significant level of cross-linguistic non-arbitrariness. In sum, the paper provides new methods to detect cross-linguistic associations at scale.

@inproceedings{pimentel-etal-2021-finding,
    author = {
        Tiago Pimentel and
        Brian Roark and
        Søren Wichmann and
        Ryan Cotterell and
        Damián Blasi
    },
    booktitle = {Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
    title = {Finding Concept-specific Biases in Form--Meaning Associations},
    year = {2021},
    doi = {10.18653/v1/2021.naacl-main.349},
    url = {https://arxiv.org/abs/2104.06325},
    pages = {4416--4425},
}