  Active learning aims to deliver maximum benefit when resources are scarce. We
use COMET-QE, a reference-free evaluation metric, to select sentences for
low-resource neural machine translation. Using Swahili, Kinyarwanda and Spanish
for our experiments, we show that COMET-QE significantly outperforms two
variants of Round Trip Translation Likelihood (RTTL) and random sentence
selection by up to 5 BLEU points for 20k sentences selected by Active Learning
on a 30k baseline. This suggests that COMET-QE is a powerful tool for sentence
selection in the very low-resource limit.
