Most AI translations sound fluent. But fluent is not always the same as accurate. In legal contexts, where there’s no room for error, that can be a problem. A single mistranslated word in a contract can cause massive financial losses and a small error in a patent document can jeopardize years of research and innovation.
That’s why the translation industry relies on benchmarks like the BLEU score. It measures how closely machine translations align with a human-translated counterpart, providing a standardized signal of quality.
So what exactly is the BLEU score, and how much can it really tell us? In this piece, we explain how BLEU works, what it misses, and how we incorporate it into a broader, more rigorous process for evaluating and improving AI translation quality in high-stakes legal work.
What is the BLEU Score?
BLEU (short for Bilingual Evaluation Understudy) is a way to measure how closely a machine translation matches a human one.
Here’s how it works: BLEU compares the machine output to one or more human translations and checks how many small word sequences match. These short sequences are called n-grams—they might be one word, two words, or more.
If the human version says “I ate an apple” and the machine says the same, that’s a strong match. If the machine says “I ate fruit,” fewer pieces line up, and the score drops.
There’s also something called a brevity penalty. That’s BLEU’s way of catching translations that are technically correct but incomplete. For example, if the original sentence is “I ate an apple after lunch,” and the AI only says “I ate,” it shouldn’t get full credit just for matching just a couple words. So BLEU lowers the score when translations are too short.
The final result is a score from 0 to 100—but interpreting it depends on context. This chart from Google illustrates how quality levels and BLEU scores align:
| BLEU Score | Interpretation |
| < 10 | Almost useless |
| 10–19 | Hard to get the gist |
| 20–29 | The gist is clear, but has significant errors |
| 30–40 | Understandable to good translations |
| 40–50 | High quality translations |
| 50–60 | Very high quality, fluent and adequate |
| > 60 | Often better than human |
For legal content, we set the bar very high. At Bering Lab, our legal-domain BLEU scores often land between 50 and 60+—consistently outperforming tools like Google Translate, DeepL, and Papago.
And in legal work, that kind of margin matters.
Where Quality Matters (and Where It Doesn’t)
On the other hand, not every document needs that level of quality.
For example, if you’re reviewing hundreds of thousands of documents during e-discovery, you’re usually just scanning for relevance, at least at first. The goal is speed and comprehension. But if a document is flagged as potentially important, the translation will need to be reviewed more carefully to confirm its accuracy and support legal analysis.
The same goes for internal emails, background research, or early-stage drafts. These are situations where you just need to understand the gist: who’s involved, what happened, and whether it’s worth a closer look.
The requirement is much higher for contracts, patents, regulatory filings, or client-facing disclosures. These are documents where a mistranslation can introduce liability, change obligations, or even void enforceability. For that kind of content, accuracy and quality are non-negotiable. unting edits needed to align with reference translations) are often used alongside BLEU.
What BLEU Misses
BLEU is a useful way to gauge machine translation quality, which is why we use it to benchmark our AI translations. It’s fast, automated, and it gives us a consistent way to track how closely the AI output matches human reference translations. But it also has limits.
BLEU doesn’t understand the meaning of what it evaluates. It’s checking for surface-level overlap: shared words and short word sequences.
So it doesn’t factor in elements like:
- Synonyms and paraphrasing: BLEU looks for exact word matches. So if a machine translation uses different—but still correct—phrasing, it may be penalized. For example, “terminate the agreement” and “end the contract” mean the same thing legally, but BLEU sees them as mismatches because the words don’t align exactly.
- Changes in the nuances of obligation: BLEU doesn’t understand the legal weight of specific words. If a machine translates “must” as “may,” it might still get a high score, because most of the surrounding words match. But legally, that small change can completely alter the meaning of the clause.
- Jurisdiction-specific phrasing: BLEU doesn’t know if the language aligns with local legal conventions.
- Context and structure: BLEU penalizes changes in word order, even when the meaning is unchanged. That can unfairly lower scores for valid translations and at the same time miss cases where word order actually alters legal meaning.
A complete view of translation quality—especially in legal settings—requires going beyond what BLEU can measure. That’s why we always pair high BLEU scores with human legal review.
Our reviewers check for terminology accuracy, clause structure, and legal intent. They align phrasing with jurisdictional norms and make sure the output reads like a document a lawyer would trust.
In short: BLEU tells us if the AI is getting close. Human experts make sure it gets it right.
How We Deliver High-Quality Legal Translations
Getting high BLEU scores is one part of the puzzle, but consistently producing legally accurate translations takes more than just AI, and even AI + human review.
It starts with the right inputs. AI performs better when it’s trained and supported by high-quality assets. For legal content, that means:
- Glossaries: Lists of approved translations for key legal terms. These help the AI translation model stay consistent with how terms are defined across documents and how lawyers expect them to be used.
- Style guides: Rules for tone, formatting, punctuation, and structure. Legal documents often follow specific conventions (like where to place definitions or how to format clauses), and style guides help enforce those rules.
- Clean translation memories (TMs): These are databases of past human translations. When properly maintained, they give the AI useful reference points, reducing errors and improving consistency across large projects.
Without these resources, even the best AI will make avoidable mistakes. With them, translation becomes faster, more consistent, and more legally reliable.
This is part of why BeringAI scores higher than general-purpose tools. It’s not just the engine, it’s the legal-specific assets behind it.
AI-Powered Legal Translations, Reviewed by Real Lawyers
We use AI to make translation faster. We use linguistic assets like those described above to make it more accurate. But with so much at stake, automated tools are not enough.
Bering Lab’s BeringAI+ translation uses a layered review process to guarantee quality. After our proprietary AI translation model produces a first draft, every translation is reviewed by a trained legal professional. These aren’t general translators—they’re bilingual attorneys with subject matter expertise in areas like corporate law, taxation, arbitration, energy, and regulatory filings.
We’ve built a network of more than 500 lawyer-linguists, each vetted through rigorous sample tests, interviews, and reference checks. They know how to spot issues BLEU and other automated frameworks miss, including terminology misuse, ambiguous phrasing, jurisdiction-specific errors, and subtle shifts in legal meaning.
This human-in-the-loop model combines the speed of AI with the precision of legal expertise, for legal translations that are three times faster and 40% more affordable than traditional providers.
The Bottom Line
The legal sector (and regulated industries in general), have been slow to embrace AI translation, and with good reason. Metrics like BLEU help us define quality so that we can build a process that drives it, while still giving these industries the benefits of AI translation.
If your translations need to be fast, affordable, and legally accurate, BeringAI+ delivers all three.
Get in touch to see how we can support your next legal project.
