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

Author:

Clark Thomas Hikaru1,Meister Clara2,Pimentel Tiago3,Hahn Michael4,Cotterell Ryan5,Futrell Richard6,Levy Roger7

Affiliation:

1. MIT, USA. thclark@mit.edu

2. ETH Zürich, Switzerland. meistecl@inf.ethz.ch

3. University of Cambridge, UK. tp472@cam.ac.uk

4. Saarland University, Germany. mhahn@lst.uni-saarland.de

5. ETH Zürich, Switzerland. ryan.cotterell@inf.ethz.ch

6. UC Irvine, USA. rfutrell@uci.edu

7. MIT, USA. rplevy@mit.edu

Abstract

Abstract While natural languages differ widely in both canonical word order and word order flexibility, their word orders still follow shared cross-linguistic statistical patterns, often attributed to functional pressures. In the effort to identify these pressures, prior work has compared real and counterfactual word orders. Yet one functional pressure has been overlooked in such investigations: The uniform information density (UID) hypothesis, which holds that information should be spread evenly throughout an utterance. Here, we ask whether a pressure for UID may have influenced word order patterns cross-linguistically. To this end, we use computational models to test whether real orders lead to greater information uniformity than counterfactual orders. In our empirical study of 10 typologically diverse languages, we find that: (i) among SVO languages, real word orders consistently have greater uniformity than reverse word orders, and (ii) only linguistically implausible counterfactual orders consistently exceed the uniformity of real orders. These findings are compatible with a pressure for information uniformity in the development and usage of natural languages.1

Publisher

MIT Press

Subject

Artificial Intelligence,Computer Science Applications,Linguistics and Language,Human-Computer Interaction,Communication

Reference76 articles.

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3. Testing the processing hypothesis of word order variation using a probabilistic language model;Bloem,2016

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5. Information density and dependency length as complementary cognitive models;Collins;Journal of Psycholinguistic Research,2014

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