Emergent Word Order Universals from Cognitively-Motivated Language Models

Anonymous

Abstract: The world's languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) word order typically employs postpositions. Explaining the source of such biases is a key goal in linguistics.We study the 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 these cognitive biases and predictability (perplexity) can explain many aspects of word-order universals.This also showcases the advantage of cognitively-motivated LMs, which are typically employed in cognitive modeling, in the computational simulation of language universals.

Paper Type: long

Research Area: Linguistic theories, Cognitive Modeling and Psycholinguistics

Contribution Types: Model analysis & interpretability, Theory

Languages Studied: Artificial languages (to study the language universals among 1616 attested languages)