# Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality

arXiv: https://arxiv.org/abs/2206.04239 · HTML: https://mlsanity.com/paper/2206.04239/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 187 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Formal Limitations on the Measurement of Mutual Information](https://mlsanity.com/paper/1811.04251/) (2018)
- [Geodesic Exponential Kernels: When Curvature and Linearity Conflict](https://mlsanity.com/paper/1411.0296/) (2014)
- [Color naming reflects both perceptual structure and communicative need](https://mlsanity.com/paper/1805.06165/) (2018)
- [The placement of the head that maximizes predictability. An information theoretic approach](https://mlsanity.com/paper/1705.09932/) (2017)
- [Human languages order information efficiently](https://mlsanity.com/paper/1510.02823/) (2015)
- [Sentences with Gapping: Parsing and Reconstructing Elided Predicates](https://mlsanity.com/paper/1804.06922/) (2018)

## Built on by (most cited first, 10 of 10)

- [A Cross-Linguistic Pressure for Uniform Information Density in Word Order](https://mlsanity.com/paper/2306.03734/) (2023)
- [Strategic resource allocation in memory encoding: An efficiency principle shaping language processing](https://mlsanity.com/paper/2503.14728/) (2025)
- [The taggedPBC: Annotating a massive parallel corpus for crosslinguistic investigations](https://mlsanity.com/paper/2505.12560/) (2025)
- [Word length predicts word order: "Min-max"-ing drives language evolution](https://mlsanity.com/paper/2505.13913/) (2025)
- [Multiple evolutionary pressures shape identical consonant avoidance in the world's languages](https://mlsanity.com/paper/2309.14006/) (2023)
- [Word Order and World Knowledge](https://mlsanity.com/paper/2403.00876/) (2024)
- [Unification of Balti and trans-border sister dialects in the essence of LLMs and AI Technology](https://mlsanity.com/paper/2411.13409/) (2024)
- [Vocabulary shapes cross-lingual variation of word-order learnability in language models](https://mlsanity.com/paper/2603.19427/) (2026)
- [ChronoLens: Measuring Language Change Across Time, Languages, and Linguistic Levels](https://mlsanity.com/paper/2608.03507/) (2026)
- [Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations](https://mlsanity.com/paper/2609.06025/) (2026)

## Abstract (arXiv)

Languages vary considerably in syntactic structure. About 40% of the world's languages have subject-verb-object order, and about 40% have subject-object-verb order. Extensive work has sought to explain this word order variation across languages. However, the existing approaches are not able to explain coherently the frequency distribution and evolution of word order in individual languages. We propose that variation in word order reflects different ways of balancing competing pressures of dependency locality and information locality, whereby languages favor placing elements together when they are syntactically related or contextually informative about each other. Using data from 80 languages in 17 language families and phylogenetic modeling, we demonstrate that languages evolve to balance these pressures, such that word order change is accompanied by change in the frequency distribution of the syntactic structures which speakers communicate to maintain overall efficiency. Variability in word order thus reflects different ways in which languages resolve these evolutionary pressures. We identify relevant characteristics that result from this joint optimization, particularly the frequency with which subjects and objects are expressed together for the same verb. Our findings suggest that syntactic structure and usage across languages co-adapt to support efficient communication under limited cognitive resources.