COMET-QE and Active Learning for Low-Resource Machine Translation
This addresses the challenge of improving machine translation efficiency in low-resource settings, though it is incremental as it builds on existing active learning and evaluation metrics.
The paper tackled the problem of selecting sentences for low-resource neural machine translation using COMET-QE, a reference-free evaluation metric, and showed that it outperforms other methods by up to 5 BLEU points in experiments with Swahili, Kinyarwanda, and Spanish.
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.