COMET-QE and Active Learning for Low-Resource Machine Translation
Everlyn Asiko Chimoto, Bruce A. Bassett · 2022
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.