  High-performing machine translation (MT) systems can help overcome language
barriers while making it possible for everyone to communicate and use language
technologies in the language of their choice. However, such systems require
large amounts of parallel sentences for training, and translators can be
difficult to find and expensive. Here, we present a data collection strategy
for MT which, in contrast, is cheap and simple, as it does not require
bilingual speakers. Based on the insight that humans pay specific attention to
movements, we use graphics interchange formats (GIFs) as a pivot to collect
parallel sentences from monolingual annotators. We use our strategy to collect
data in Hindi, Tamil and English. As a baseline, we also collect data using
images as a pivot. We perform an intrinsic evaluation by manually evaluating a
subset of the sentence pairs and an extrinsic evaluation by finetuning mBART on
the collected data. We find that sentences collected via GIFs are indeed of
higher quality.
