Translation from the Information Bottleneck Perspective: an Efficiency Analysis of Spatial Prepositions in Bitexts

TL;DR

This paper models translation as an Information Bottleneck optimization, analyzing spatial prepositions across languages to reveal that human translation aligns with efficiency principles predicted by IB theory.

Contribution

It introduces a novel IB-based framework for analyzing translation, applying it to spatial prepositions across multiple languages using bitexts.

Findings

Translations of prepositions are closer to the IB optimal frontier than counterfactuals.

A predictive model of similarity judgements achieved a Spearman correlation of 0.78.

Evidence suggests human translators exhibit communicative efficiency pressures.

Abstract

Efficient communication requires balancing informativity and simplicity when encoding meanings. The Information Bottleneck (IB) framework captures this trade-off formally, predicting that natural language systems cluster near an optimal accuracy-complexity frontier. While supported in visual domains such as colour and motion, linguistic stimuli such as words in sentential context remain unexplored. We address this gap by framing translation as an IB optimisation problem, treating source sentences as stimuli and target sentences as compressed meanings. This allows IB analyses to be performed directly on bitexts rather than controlled naming experiments. We applied this to spatial prepositions across English, German and Serbian translations of a French novel. To estimate informativity, we conducted a pile-sorting pilot-study (N=35) and obtained similarity judgements of pairs of prepositions. We trained a low-rank projection model (D=5) that predicts these judgements (Spearman correlation: 0.78). Attested translations of prepositions lie closer to the IB optimal frontier than counterfactual alternatives, offering preliminary evidence that human translators exhibit communicative efficiency pressure in the spatial domain. More broadly, this work suggests that translation can serve as a window into the cognitive efficiency pressures shaping cross-linguistic semantic systems.