𝘈𝘱𝘱𝘳𝘰𝘹 𝘳𝘦𝘢𝘥 𝘵𝘪𝘮𝘦: 5 𝘮𝘪𝘯𝘴🕒
When it comes to translation, accuracy is everything. But what does “accuracy” actually mean in practice, and how do we measure it?
If you’ve ever compared a human translation with a machine-generated version (like we often do), you’ve likely spotted the difference in tone, clarity or nuance.
In this blog, we take a closer look at what academic studies and professional evaluations tell us about translation accuracy, especially in the context of machine translation (MT) versus human linguists.
Whether you’re managing multilingual regulatory content, marketing copy or legal documentation, translation accuracy can impact your compliance, brand reputation, and even customer safety.
And just to be clear, Wolfestone offers solutions across machine translation, machine translation post-editing and human translation. But, we'll always be honest about accuracy and where each solution is best suited.
Can machines really match humans?
In 2018, researchers at Microsoft famously claimed that machine translation had achieved "human parity" for certain Chinese-to-English news articles. This headline travelled far and wide, but it didn’t tell the full story.
Subsequent analysis, including a 2018 study by Läubli et al., challenged this conclusion by using a more realistic test setup: having professional translators evaluate full documents, rather than just isolated sentences.
The result? Human translations were clearly preferred when raters had proper context.
In a pairwise ranking experiment, human raters assessing adequacy and fluency show a stronger preference for human over machine translation when evaluating documents as compared to isolated sentences.
Later work, including this 2021 MQM evaluation study, confirmed that when you assess translations using robust quality metrics like Multidimensional Quality Metrics (MQM), and give raters full context, human output consistently outperforms MT in fluency, adequacy and error count.
The problem with sentence-level evaluation
A major challenge in comparing human and machine translation lies in how people have tested them.
Many early MT benchmarks relied on sentence-level evaluations, ignoring the way meaning and tone build across a paragraph or document.
Research shows that this method skews results in favour of machines.
The same authors of the 2021 MQM study argued for a document-level evaluation model, showing that machine parity isn't perhaps true when translations are assessed in full context.
Industry-specific studies: Accuracy depends on the use case
As we often advise clients, translation services are never one-size-fits-all.
In domains like healthcare, legal and literature, even small errors can carry significant consequences.
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In a 2023 study comparing medical texts and poetry, researchers found that while MT could roughly handle straightforward health awareness materials, it performed poorly on stylistic or metaphorical content, such as poetry.
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Another peer-reviewed paper in PMC evaluated legal Arabic-to-English translations. Human translators outperformed AI in both accuracy and style, especially in high-stakes content where ambiguity could be costly.
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In healthcare settings, a BMJ Quality & Safety article found that while machine-translated patient instructions had a relatively low error rate (~6%), those errors could still pose serious safety risks without human post-editing.
These studies reflect a consistent trend: machine translation may be acceptable in some settings, but it still can’t be relied upon in high-accuracy domains without expert human oversight.
Post-editing: The human-in-the-loop approach
So, where does that leave us?
For Wolfestone, the most practical middle ground is Machine Translation Post-Editing (MTPE), a workflow where professional linguists review, edit and improve machine-generated output.
Studies such as the MQM error analysis and industry papers show that post-editing can significantly improve both speed and cost-effectiveness without sacrificing quality, as long as it’s done by qualified linguists familiar with the subject matter.
But post-editing isn’t a shortcut. It works best when:
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The content is not highly technical
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You have a clear style guide or glossary
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Linguists are trained to identify machine-specific errors (e.g. literalism, omitted negatives and mistranslations of idioms)
Translation accuracy in the real world
So, what’s the takeaway from all this research?
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Translation accuracy is complex and context-dependent.
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Human translation still leads in fluency, consistency, and handling nuance, especially in sensitive or creative content.
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Machine translation is improving fast, but it’s not a replacement for human linguists as it stands. However, it can be used in some cases.
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The most effective approach is hybrid: choosing the right service level for each use case, with human expertise guiding quality at every stage.
At Wolfestone, we stay at the forefront of both human and AI translation workflows.
Our ISO 17100-certified processes, subject-matter linguists, and commitment to quality assurance mean your content is always in expert hands, regardless of the technology used.
If you’re weighing up machine versus human translation, or just want to know how to improve your multilingual content, we’re happy to help.
We offer free consultations and test pieces so you can compare options side-by-side.
Get in touch today and let us help you find the right level of accuracy for your content.
𝘒𝘦𝘪𝘳𝘢𝘯 𝘩𝘢𝘴 𝘣𝘦𝘦𝘯 𝘸𝘳𝘪𝘵𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘭𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯𝘴 𝘴𝘪𝘯𝘤𝘦 2021 𝘢𝘯𝘥 𝘪𝘴 𝘤𝘰𝘮𝘮𝘪𝘵𝘵𝘦𝘥 𝘵𝘰 𝘩𝘦𝘭𝘱𝘪𝘯𝘨 𝘣𝘳𝘢𝘯𝘥𝘴 𝘨𝘰 𝘨𝘭𝘰𝘣𝘢𝘭 𝘢𝘯𝘥 𝘮𝘢𝘳𝘬𝘦𝘵 𝘴𝘮𝘢𝘳𝘵. 𝘏𝘦 𝘪𝘴 𝘯𝘰𝘸 𝘵𝘩𝘦 𝘏𝘦𝘢𝘥 𝘰𝘧 𝘔𝘢𝘳𝘬𝘦𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘰𝘷𝘦𝘳𝘴𝘦𝘦𝘴 𝘢𝘭𝘭 𝘰𝘧 𝘰𝘶𝘳 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 𝘵𝘰 𝘦𝘯𝘴𝘶𝘳𝘦 𝘸𝘦 𝘱𝘳𝘰𝘷𝘪𝘥𝘦 𝘷𝘢𝘭𝘶𝘢𝘣𝘭𝘦, 𝘶𝘴𝘦𝘧𝘶𝘭 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 𝘵𝘰 𝘢𝘶𝘥𝘪𝘦𝘯𝘤𝘦𝘴.
