Work Smarter...Not Harder: Efficient Minimization of Dependency Length in SOV Languages

TL;DR

This paper investigates how SOV languages minimize dependency length through a simple, efficient heuristic of moving short preverbal constituents next to the main verb, supported by large-scale corpus evidence.

Contribution

It proposes a new mechanistic explanation for dependency length minimization based on a heuristic strategy rather than global optimization, supported by corpus data across multiple languages.

Findings

Preverbal constituent movement explains ordering better than global minimization.

Heuristic strategy reduces dependency length efficiently.

Supports bounded rationality in language processing.

Abstract

Dependency length minimization is a universally observed quantitative property of natural languages. However, the extent of dependency length minimization, and the cognitive mechanisms through which the language processor achieves this minimization remain unclear. This research offers mechanistic insights by postulating that moving a short preverbal constituent next to the main verb explains preverbal constituent ordering decisions better than global minimization of dependency length in SOV languages. This approach constitutes a least-effort strategy because it's just one operation but simultaneously reduces the length of all preverbal dependencies linked to the main verb. We corroborate this strategy using large-scale corpus evidence across all seven SOV languages that are prominently represented in the Universal Dependency Treebank. These findings align with the concept of bounded rationality, where decision-making is influenced by 'quick-yet-economical' heuristics rather than exhaustive searches for optimal solutions. Overall, this work sheds light on the role of bounded rationality in linguistic decision-making and language evolution.