mlsanityData to 7 Oct 2026

COMET-QE and Active Learning for Low-Resource Machine Translation

arXiv 2210.15696 ↗First posted 27 Oct 2022cs.CLcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 8 of its 23 references that are in the map. First posted on arXiv 27 Oct 2022.

Builds on

8 of 23 references.

References · 8

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  2. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  3. 2020Participatory Research for Low-resourced Machine Translation: A Case Study in African Languagescited by 159
  4. 2020Unbabel's Participation in the WMT20 Metrics Shared Taskcited by 56
  5. 2019Joey NMT: A Minimalist NMT Toolkit for Novicescited by 39
  6. 2018Active Learning for Interactive Neural Machine Translation of Data Streamscited by 20
  7. 2021Phrase-level Active Learning for Neural Machine Translationcited by 13
  8. 2014Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translationcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 2.

10 papers.

  1. 2025BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translationcited by 21
  2. 2025Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Aheadcited by 15
  3. 2024Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruningcited by 10
  4. 2025Reference-free Evaluation Metrics for Text Generation: A Surveycited by 9
  5. 2025Meaningful Pose-Based Sign Language Evaluationcited by 6
  6. 2023Active Learning for Natural Language Generationcited by 3
  7. 2024Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languagescited by 3
  8. 2024Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Paircited by 3
  9. 2026Testing the Assumptions of Active Learning for Translation Tasks with Few Samplescited by 0
  10. 2026FACET at WMT 2026 Automated Translation Quality Evaluation Taskcited by 0