Finding Concept-specific Biases in Form–Meaning Associations

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,
    title = "Finding Concept-specific Biases in Form--Meaning Associations",
    author = "Pimentel, Tiago  and
      Roark, Brian  and
      Wichmann, S{\o}ren  and
      Cotterell, Ryan  and
      Blasi, Dami\'{a}n",
    booktitle = "Proceedings of the 2021 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2021",
    address = "Virtual",
    publisher = "Association for Computational Linguistics",
    url={https://arxiv.org/abs/2104.06325}
}