The Accuracy of AI Translation in the Top 10 Language Combinations
With the advent of Large Language Models, AI Translation or Machine Translation (MT), has improved significantly. But even as the tech jumps ahead, accuracy is still a challenge. Some platforms do claim to deliver up to 90% accuracy, which sounds great… until that ambiguous 10% leads to a legal claim. So how do you assess when you can rely on the machine and when you need a human expert?
There is no publicly available, robust “table of accuracy rates” that covers the top 10 language combinations in the translation industry, in a reliable way, for several reasons. But we can sketch a working approximation (with caveats).
Why Accuracy is Difficult to Measure
- “Accuracy” in AI Translation is hard to define: automated metrics (like BLEU score, COMET, METEOR) measure n-gram overlap or surface similarity to a reference translation, but they don’t necessarily reflect meaning, style, or cultural nuance.
- Performance varies widely depending on the language pair, direction (A → B vs. B → A), domain (news, technical, literary, legal), and even the specific text.
- There’s no standardized public dataset giving “industry-wide average accuracy by language pair.”
That said, based on publicly available sources, we can produce an indicative table showing approximate “accuracy ranges” (i.e. how good AI Translation tends to be) for language pairs commonly seen among the top language combinations in the translation industry.
Approximate Accuracy of MT/AI Translation for Common Translation Pairings
| Rank | Language Pair | Accuracy / Quality Range* |
|---|---|---|
| 1 | English ↔ Spanish | ~80-90% for major-language content (factual, straightforward). |
| 2 | English ↔ French | ~80-90% under favorable conditions. |
| 3 | English ↔ German | ~80-90% for many texts; tends to perform well among Western-European pairs. |
| 4 | English ↔ Portuguese (e.g. Brazilian) | Likely comparable to English–Spanish or English–French (i.e. high 70s to 90s % range), given relatively abundant data and linguistic similarity within Romance languages. |
| 5 | English ↔ Russian | Lower than Western European pairs — perhaps in the 60–80% range depending on domain and text complexity. (Given the relative complexity and fewer parallel corpora.) |
| 6 | English ↔ Arabic | Likely 60–80% (or lower for complex/idiomatic content), recognizing greater structural and cultural differences vs. European languages. |
| 7 | English ↔ Chinese (Simplified) | Common but performance generally lags behind European-family pairs — maybe 60–75%, with variability depending on domain (technical, idiomatic, or formal language more challenging). |
| 8 | Romance-to-Romance (e.g. Spanish ↔ Portuguese, Spanish ↔ French, Portuguese ↔ Italian) | Often fairly good — 70–90%, depending heavily on readability, similarity, domain. Romance language similarity helps. |
| 9 | Western European less-common pairs (e.g. English ↔ Italian) | Likely in the 75–85% range for general text, though with greater variation by domain. (Based on general strengths of MT for Western-European languages.) |
| 10 | Mixed / lower-resource pairs (e.g. languages outside major European / global ones) | Often 55–70%, sometimes lower depending on data availability, linguistic distance, domain complexity. |
* “Accuracy / Quality Range” is a rough, heuristic estimate based on industry-wide observations, not a rigorous, peer-reviewed statistic.
“Quality” refers to the probability that a machine-translated output will be acceptable or useful with minimal post-editing, for typical factual or general content.
Key Caveats and What “Accuracy” Means (or Doesn’t)
- Metrics like BLEU / COMET / METEOR measure surface similarity (n-gram overlap or semantic closeness) with a reference human translation. They don’t guarantee semantic adequacy, stylistic fidelity, or cultural appropriateness.
- What works “well” is heavily dependent on text type: factual / technical / formulaic content tends to translate better than idiomatic, literary or highly creative content. Industry sources note that MT for “major languages” can hit ~ 80-90% accuracy for favorable content, but drop significantly for less common languages or more complex texts.
- For professional use (legal, medical, marketing, literary) many translation providers combine Machine/AI translation (MT) with human post-editing (MTPE) or use Computer-Assisted human Translation (CAT tools) including Translation Memory (TM) to create MT + CAT + TM workflows. These workflows typically achieve much higher “accuracy” (often reported as 90-98% for CAT + human).
What This Means for Translation Industry Practice
In the end, the real question is not whether AI translation is perfect (we know it’s not). The challenge is understanding how to measure its strengths, spot its blind spots, and perhaps most importantly: how to know when a human touch is not just helpful, but necessary.
- AI translation can be quite reliable for many of the top language pairs (especially Western-European ones) — often good enough for first drafts, content triage, or non-critical translations.
- For critical, nuanced, or high-stakes translations (legal, marketing, creative, medical, technical) — human translation or human post-editing remains essential.
- Many translation service providers are already using hybrid workflows (MT + human review / CAT) to balance speed, cost, and quality; this seems the most realistic approach given current MT performance.
At Quicksilver Translate, we offer different levels of translation quality and pricing, depending on the type of content you are translating, and its business goal. We often work with hybrid workflows — our editing team have considerable experience with post-editing and review.