  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.
