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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.348</identifier>
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%0 Conference Proceedings
%T COMET-QE and Active Learning for Low-Resource Machine Translation
%A Chimoto, Everlyn
%A Bassett, Bruce
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chimoto-bassett-2022-comet
%X 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.
%R 10.18653/v1/2022.findings-emnlp.348
%U https://aclanthology.org/2022.findings-emnlp.348/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.348
%P 4735-4740

Markdown (Informal)

[COMET-QE and Active Learning for Low-Resource Machine Translation](https://aclanthology.org/2022.findings-emnlp.348/) (Chimoto & Bassett, Findings 2022)

• COMET-QE and Active Learning for Low-Resource Machine Translation (Chimoto & Bassett, Findings 2022)

ACL

• Everlyn Chimoto and Bruce Bassett. 2022. COMET-QE and Active Learning for Low-Resource Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4735–4740, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.