@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Emergent Word Order Universals from Cognitively-Motivated Language Models
%A Kuribayashi, Tatsuki
%A Ueda, Ryo
%A Yoshida, Ryo
%A Oseki, Yohei
%A Briscoe, Ted
%A Baldwin, Timothy
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F kuribayashi-etal-2024-emergent
%X 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.
%R 10.18653/v1/2024.acl-long.781
%U https://aclanthology.org/2024.acl-long.781/
%U https://doi.org/10.18653/v1/2024.acl-long.781
%P 14522-14543
Markdown (Informal)
[Emergent Word Order Universals from Cognitively-Motivated Language Models](https://aclanthology.org/2024.acl-long.781/) (Kuribayashi et al., ACL 2024)
ACL
- Tatsuki Kuribayashi, Ryo Ueda, Ryo Yoshida, Yohei Oseki, Ted Briscoe, and Timothy Baldwin. 2024. Emergent Word Order Universals from Cognitively-Motivated Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14522–14543, Bangkok, Thailand. Association for Computational Linguistics.