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

arXiv: https://arxiv.org/abs/2210.15696 · HTML: https://mlsanity.com/paper/2210.15696/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [Unbabel's Participation in the WMT20 Metrics Shared Task](https://mlsanity.com/paper/2010.15535/) (2020)
- [Joey NMT: A Minimalist NMT Toolkit for Novices](https://mlsanity.com/paper/1907.12484/) (2019)
- [Active Learning for Interactive Neural Machine Translation of Data Streams](https://mlsanity.com/paper/1807.11243/) (2018)
- [Phrase-level Active Learning for Neural Machine Translation](https://mlsanity.com/paper/2106.11375/) (2021)
- [Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation](https://mlsanity.com/paper/1410.5877/) (2014)

## Built on by (most cited first, 10 of 10)

- [BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation](https://mlsanity.com/paper/2502.04314/) (2025)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning](https://mlsanity.com/paper/2405.19462/) (2024)
- [Reference-free Evaluation Metrics for Text Generation: A Survey](https://mlsanity.com/paper/2501.12011/) (2025)
- [Meaningful Pose-Based Sign Language Evaluation](https://mlsanity.com/paper/2510.07453/) (2025)
- [Active Learning for Natural Language Generation](https://mlsanity.com/paper/2305.15040/) (2023)
- [Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages](https://mlsanity.com/paper/2404.02512/) (2024)
- [Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair](https://mlsanity.com/paper/2404.12299/) (2024)
- [Testing the Assumptions of Active Learning for Translation Tasks with Few Samples](https://mlsanity.com/paper/2604.08977/) (2026)
- [FACET at WMT 2026 Automated Translation Quality Evaluation Task](https://mlsanity.com/paper/2610.00096/) (2026)

## 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.