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A Cross-Linguistic Pressure for Uniform Information Density in Word Order

Paper · 2023 · Thomas Hikaru Clark, Clara Meister, Tiago Pimentel et al.

Summary

Languages differ in word order but share statistical tendencies, which are often explained by functional pressures such as dependency length minimization. This paper asks whether uniform information density (UID) also shapes word order. UID is the idea that communication works best when information is spread evenly, without sharp peaks. The authors took dependency-parsed sentences from 10 typologically diverse languages, using UD relations with function words treated as heads. They rearranged each sentence under several counterfactual grammars, building on the procedure of Hahn et al. (2020). Transformer language models trained on the real and counterfactual corpora supplied per-word surprisal, from which three uniformity scores were derived. In SVO languages, attested orders were consistently more even than their reversed versions. Only an implausible variant that sorts words by frequency reliably beat real orders, and it does so at a large cost in expressivity. Verb-before-object grammars were generally smoother than object-before-verb ones. The findings point to a UID pressure acting through both grammar and speakers' choices, and they offer a possible account of why SVO is so widespread.

Key points

  • UID measures: Uniformity is scored three ways: within-sentence surprisal variance (UID_v), average squared word-to-word surprisal change (UID_lv), and a power mean with k > 1 (UID_p). Lower values mean greater uniformity.
  • Counterfactual grammars: The tested variants are REVERSE, EFFICIENT-VO and EFFICIENT-OV, MIN-DL-LOC and MIN-DL-OPT, RANDOM1–RANDOM5, and SORT-FREQ.
  • VO vs. OV: EFFICIENT-VO had lower surprisal variance than EFFICIENT-OV in 9 of 10 languages.
  • SOV languages: The preference for REAL over REVERSE did not hold consistently for Turkish, Hindi and Farsi. The authors attribute this to basic word order and speaker choices pulling in opposite directions.
  • Robustness checks:
    • The main corpus is Wiki40b, and a subset of languages was rerun on CC100.
    • Analyses restricted to document-initial sentences largely kept the REAL < REVERSE variance pattern.
    • Code is available at github.com/thomashikaru/word-order-uid.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of CambridgeCambridge · United Kingdom256 works · 123,373 citations🎓Massachusetts Institute of TechnologyCambridge · United States243 works · 76,506 citations🎓Saarland UniversitySaarbrücken · Germany149 works · 18,349 citations🎓ETH ZurichZurich · Switzerland84 works · 9,181 citations🎓University of California, IrvineIrvine · United States43 works · 36,565 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPsychology · 1,649 worksDevelopmental and Educational Psychology · 523 works

Cite

Cite as Thomas Hikaru Clark, Clara Meister, Tiago Pimentel, Michael Hahn, Ryan Cotterell, Richard Futrell and Roger Levy. 2023. A Cross-Linguistic Pressure for Uniform Information Density in Word Order. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00589

@article{clark2023cross,
  title = {{A Cross-Linguistic Pressure for Uniform Information Density in Word Order}},
  author = {Thomas Hikaru Clark, Clara Meister, Tiago Pimentel, Michael Hahn, Ryan Cotterell, Richard Futrell and Roger Levy},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.1162/tacl\_a\_00589},
  url = {https://doi.org/10.1162/tacl_a_00589}
}

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