language benchmark
Translation en→Set1 COMET22
COMET-22 is an ensemble machine translation evaluation metric combining a COMET estimator model trained with Direct Assessments and a multitask model that predicts sentence-level scores and word-level OK/BAD tags. It demonstrates improved correlations compared to state-of-the-art metrics and increased robustness to critical errors.
Updated Aug 7, 2026
Published models3
Registry coverage3
MetricScore
EvidenceB
Translation en→Set1 COMET22 leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | AM Nova ProAmazon | 89.1% | 100.0% | 3 | C | |
| 2 | AMNova LiteAmazon | 88.8% | 50.0% | 3 | C | |
| 3 | AMNova MicroAmazon | 88.5% | 0.0% | 3 | C |
Score distribution
Top published rows on the benchmark's original scale.
Translation en→Set1 COMET22 highlights
The top published results on this benchmark's own scale.
What is Translation en→Set1 COMET22?
Definition and scoring fields from the benchmark registry.
COMET-22 is an ensemble machine translation evaluation metric combining a COMET estimator model trained with Direct Assessments and a multitask model that predicts sentence-level scores and word-level OK/BAD tags. It demonstrates improved correlations compared to state-of-the-art metrics and increased robustness to critical errors.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- Translation en→Set1 COMET22
- Modality
- text
- Primary category
- language
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- translation-en→set1-comet22|llm-stats-current
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
FAQ
Common questions about Translation en→Set1 COMET22.
Which model scores highest on Translation en→Set1 COMET22?
Nova Pro is currently ranked first with 89.1%.
What does Translation en→Set1 COMET22 measure?
COMET-22 is an ensemble machine translation evaluation metric combining a COMET estimator model trained with Direct Assessments and a multitask model that predicts sentence-level scores and word-level OK/BAD tags. It demonstrates improved correlations compared to state-of-the-art metrics and increased robustness to critical errors.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
3 unique published model results are currently shown.
Does this benchmark affect the overall score?
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.