# Cross-linguistic differences and similarities in image descriptions

arXiv: https://arxiv.org/abs/1707.01736 · HTML: https://mlsanity.com/paper/1707.01736/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2017.

## Built on by (most cited first, 12 of 12)

- [Visually Grounded Reasoning across Languages and Cultures](https://mlsanity.com/paper/2109.13238/) (2021)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages](https://mlsanity.com/paper/2201.11732/) (2022)
- [Transparent Human Evaluation for Image Captioning](https://mlsanity.com/paper/2111.08940/) (2021)
- [Underspecification in Scene Description-to-Depiction Tasks](https://mlsanity.com/paper/2210.05815/) (2022)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [Translation-Enhanced Multilingual Text-to-Image Generation](https://mlsanity.com/paper/2305.19216/) (2023)
- [Cross-Lingual and Cross-Cultural Variation in Image Descriptions](https://mlsanity.com/paper/2409.16646/) (2024)
- [Uncovering Cultural Representation Disparities in Vision-Language Models](https://mlsanity.com/paper/2505.14729/) (2025)
- [A Large-Scale Multilingual Study of Visual Constraints on Linguistic Selection of Descriptions](https://mlsanity.com/paper/2302.04811/) (2023)
- [Image captioning in different languages](https://mlsanity.com/paper/2407.09495/) (2024)
- [Using Deep Learning to Generate Semantically Correct Hindi Captions](https://mlsanity.com/paper/2602.13352/) (2026)

## Abstract (arXiv)

Automatic image description systems are commonly trained and evaluated on large image description datasets. Recently, researchers have started to collect such datasets for languages other than English. An unexplored question is how different these datasets are from English and, if there are any differences, what causes them to differ. This paper provides a cross-linguistic comparison of Dutch, English, and German image descriptions. We find that these descriptions are similar in many respects, but the familiarity of crowd workers with the subjects of the images has a noticeable influence on description specificity.