Evaluating machine translation quality used to be simple: run BLEU or TER against a reference translation. But as Large Language Models (LLMs) output fluent, highly paraphrased translations, n-gram matching metrics like BLEU penalize valid, natural phrasing that happens not to match the reference word-for-word.
Why Neural Evaluation Metrics Win
Modern localization pipelines employ neural evaluation models like COMET (Crosslingual Optimized Metric for Evaluation of Translation) and BLEURT. Unlike BLEU, COMET uses cross-lingual embeddings to evaluate semantic similarity between source, translation, and reference.
Key Differences: BLEU vs. COMET
- BLEU: Measures exact n-gram overlap. Fails when LLMs use synonyms or alter sentence structure for better flow.
- COMET: Trained on human multidimensional quality metrics (MQM). Detects mistranslations, omissions, and register mismatches even with non-identical vocabulary.
- MTQE (Quality Estimation): Evaluates translation quality without needing a reference translation, enabling real-time production filtering.
Combining Automated Metrics with MQM Human Audits
Automation filters low-hanging fruit, but enterprise brand safety requires Multidimensional Quality Metrics (MQM) human evaluations by certified linguists across critical categories: Accuracy, Fluency, Terminology, Style, and Design.
Benchmarking your translation engine? Start a project — we provide automated COMET scoring alongside expert MQM linguist audits.