Emergent Word Order Universals from Cognitively-Motivated Language Models
This addresses a key problem in linguistics by linking cognitive biases to language universals, though it is incremental in applying existing models to this domain.
The study investigated whether cognitively-motivated language models can explain word-order universals across languages, finding that typologically-typical orders have lower perplexity in these 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.