# Practical Comparable Data Collection for Low-Resource Languages via Images

arXiv: https://arxiv.org/abs/2004.11954 · HTML: https://mlsanity.com/paper/2004.11954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data](https://mlsanity.com/paper/2106.06875/) (2021)
- [LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models](https://mlsanity.com/paper/2411.13453/) (2024)
- [Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning](https://mlsanity.com/paper/2505.13628/) (2025)

## Abstract (arXiv)

We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators. Our method involves using a carefully selected set of images as a pivot between the source and target languages by getting captions for such images in both languages independently. Human evaluations on the English-Hindi comparable corpora created with our method show that 81.1% of the pairs are acceptable translations, and only 2.47% of the pairs are not translations at all. We further establish the potential of the dataset collected through our approach by experimenting on two downstream tasks - machine translation and dictionary extraction. All code and data are available at https://github.com/madaan/PML4DC-Comparable-Data-Collection.