Translation Quality Assurance for AI Output
You’ve translated your content, edited it, and scaled it across languages. But how do you know it’s actually good? Translation quality assurance (TQA) is the systematic process of evaluating whether a translation is accurate, fluent, and fit for purpose.
Automated Quality Metrics
The translation industry uses several automated metrics to score quality. The most important ones to know:
BLEU (Bilingual Evaluation Understudy) — the oldest standard, dating to 2002. It counts how many word sequences in the AI output match a reference translation. Simple and fast, but limited: it can’t tell that “the car is fast” and “the vehicle is quick” mean the same thing.
COMET (Crosslingual Optimized Metric for Evaluation of Translation) — a newer, neural approach trained on human quality judgments. It evaluates meaning preservation rather than word overlap, making it much better for modern AI translations that paraphrase naturally. Increasingly viewed as the industry standard.
chrF — measures quality at the character level rather than word level. Particularly useful for morphologically rich languages like Finnish, Turkish, or Hungarian where word forms change heavily.
These metrics require a reference translation to compare against, so they’re most useful when evaluating AI systems at scale — comparing models, tracking quality over time, or benchmarking before and after a prompt change.
Human Evaluation
For individual translations, human review remains essential. The main approaches:
- Direct assessment — a reviewer rates each segment on a quality scale (typically 1-100)
- Error annotation — a reviewer marks specific errors by type (accuracy, fluency, terminology, formatting) and severity (critical, major, minor)
- Post-editing effort — measured by how much time or how many edits a professional needs to bring the translation to publishable quality
Error annotation is the most actionable approach — it doesn’t just tell you the translation is bad, it tells you why and where.
When AI Translation Isn’t Enough
AI translation has gotten remarkably good for most content types. But there are situations where you should default to professional human translation:
- Legal contracts and regulatory filings — a mistranslation can create liability
- Medical instructions for patients — accuracy is literally a safety issue
- Brand-defining campaigns — transcreation needs cultural intuition that AI can miss
- Content in low-resource languages — AI performance drops significantly for languages with less training data
- Sworn or certified translation — many jurisdictions require a human translator’s certification
A QA Decision Framework
| Content type | Risk level | Recommended QA |
|---|---|---|
| Internal docs, knowledge bases | Low | Automated checks + spot-check samples |
| Customer-facing support content | Medium | Full post-editing + consistency review |
| Marketing and brand content | High | Transcreation + native speaker review |
| Legal, medical, regulated content | Critical | Professional human translation with certification |
The goal isn’t to eliminate AI translation for high-stakes content — it’s to match the right level of human oversight to the risk. AI gives you speed and scale. Quality assurance gives you confidence that the output is worth publishing.