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arXiv · 2402.12363

Emergent Word Order Universals from Cognitively-Motivated Language Models

Tatsuki Kuribayashi·Ryo Ueda·Ryo Yoshida·Yohei Oseki·Ted Briscoe·Timothy Baldwin
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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.

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Tatsuki Kuribayashi, Ryo Ueda, Ryo Yoshida, Yohei Oseki, Ted Briscoe, Timothy Baldwin. 2024-02-19. Emergent Word Order Universals from Cognitively-Motivated Language Models. https://arxiv.org/abs/2402.12363

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