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.
@article{arxiv.2004.11954,
title = {Practical Comparable Data Collection for Low-Resource Languages via Images},
author = {Aman Madaan and Shruti Rijhwani and Antonios Anastasopoulos and Yiming Yang and Graham Neubig},
journal= {arXiv preprint arXiv:2004.11954},
year = {2020}
}
Comments
Accepted for poster presentation at the Practical Machine Learning for Developing Countries (PML4DC) workshop, ICLR 2020