Emergent Word Order Universals from Cognitively-Motivated Language Models
<|reference_start|>Emergent Word Order Universals from Cognitively-Motivated Language Models: 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.<|reference_end|>
@article{kuribayashi2024emergent,
    title={Emergent Word Order Universals from Cognitively-Motivated Language
  Models},
    author={Tatsuki Kuribayashi, Ryo Ueda, Ryo Yoshida, Yohei Oseki, Ted Briscoe,
  Timothy Baldwin},
    journal={arXiv preprint arXiv:2402.12363},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12363},
    primaryClass={cs.CL}
}