@inproceedings{kuribayashi-etal-2024-emergent,
    title = "Emergent Word Order Universals from Cognitively-Motivated Language Models",
    author = "Kuribayashi, Tatsuki  and
      Ueda, Ryo  and
      Yoshida, Ryo  and
      Oseki, Yohei  and
      Briscoe, Ted  and
      Baldwin, Timothy",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.acl-long.781/",
    doi = "10.18653/v1/2024.acl-long.781",
    pages = "14522--14543",
    abstract = "The world{'}s languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) languages typically use postpositions. Explaining the source of such biases is a key goal of linguistics.We study word-order universals through a computational simulation with language models (LMs).Our experiments show that typologically-typical word orders tend to have lower perplexity estimated by LMs with cognitively plausible biases: syntactic biases, specific parsing strategies, and memory limitations. This suggests that the interplay of cognitive biases and predictability (perplexity) can explain many aspects of word-order universals.It also showcases the advantage of cognitively-motivated LMs, typically employed in cognitive modeling, in the simulation of language universals."
}