Emergent Word Order Universals from Cognitively-Motivated Language Models

2024/02/19 by Tatsuki Kuribayashi, Kuribayashi, Tatsuki, Ryo Ueda +9 · 2 citations
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language and cultural evolution #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2402.12363

openalex publication_date 2024/02/19 · openalex created_date 2024/02/21 · openalex updated_date 2026/07/28

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.

Cited by

• Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models
• Which Word Orders Facilitate Length Generalization in LMs? An Investigation with GCG-Based Artificial Languages

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