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We present a large, multilingual study into how vision constrains linguisticchoice, covering four languages and five linguistic properties, such as verbtransitivity or use of numerals. We propose a novel method that leveragesexisting corpora of images with captions written by native speakers, and applyit to nine corpora, comprising 600k images and 3M captions. We study therelation between visual input and linguistic choices by training classifiers topredict the probability of expressing a property from raw images, and findevidence supporting the claim that linguistic properties are constrained byvisual context across languages. We complement this investigation with a corpusstudy, taking the test case of numerals. Specifically, we use existingannotations (number or type of objects) to investigate the effect of differentvisual conditions on the use of numeral expressions in captions, and show thatsimilar patterns emerge across languages. Our methods and findings both confirmand extend existing research in the cognitive literature. We additionallydiscuss possible applications for language generation.

A Large-Scale Multilingual Study of Visual Constraints on Linguistic Selection of Descriptions