Mirela
AI hasn’t gotten so good it can translate anything instantly and flawlessly just yet, although recent reports found that these systems have indeed achieved average accuracy scores upward of 94%. So how can you detect, refine, and correct the machine’s output to make sure it is safe, accurate, and culturally sound? The answer lies in the process that takes care of this. In this article, you’re going to learn about quality control in AI-assisted translation.
About QC and how it’s measured
Quality control in AI-assisted translation is the systematic process of inspecting, evaluating, and validating AI-generated or AI-assisted translations against predefined quality requirements. These requirements are usually accuracy, completeness, terminology consistency, linguistic fluency, and compliance with client specifications. The purpose of QC is to detect, correct, or reject defects before the translated content is delivered.
To keep QC objective, localization teams use standardized framework metrics, and the industry standard is the Multidimensional Quality Metrics (MQM) framework. MQM includes dimensions for evaluating translation quality:
- Accuracy
- Fluency
- Style
- Verity
- Terminology
- Locale Convention
- Internationalization
Metrics like BLEU, TER, COMET, and BERTScore are often used to measure or benchmark AI translation quality at scale.
Common issues with AI
AI can be highly accurate in high-resource languages (English, Spanish, French, German) because the internet is flooded with data for them. With low-resource languages, the lack of training data leads to a decrease in the AI’s translation quality.
Things also get problematic when AI engines hallucinate terms or miss whole chunks of text. You’ll see this issue most often with complex formatting or code tags.
Because LLMs predict the next word based on probability, they can occasionally drop or insert a negative word if the surrounding sentence structures make a positive statement statistically more likely.
AI doesn’t understand humor, idioms, or brand names either. And when translating from a gender-neutral language to a gendered language, it shows gender bias. It sees text and attempts to translate it, but the output might be a total failure.
The AI-assisted QC workflow
Here’s what the workflow looks like:
AI-generated draft → Automated programmatic → QC Human post-editing → Continuous feedback
Automated programmatic QC
Before a human editor even looks at the text, automated programmatic checkers run through the translation management system (TMS). Machines are best at detecting low-level errors at scale: tag and variable verification, numerical integrity, and glossary violation flags.
Human post-editing
Once the draft is scrubbed programmatically, it is handed over to a professional linguist for machine translation post-editing (MTPE). The linguist’s role in QC is to analyze, comparing the source and target side-by-side to assess semantic fidelity, tone and register, and contextual relevance. Their job is not to translate.
Feedback
The corrections made by the human editor are fed back into the system, and translation memories are updated. High-quality corrections are used to train or prompt the translation AI so that it doesn’t repeat the same mistakes in the next project.
What works best in practice
Hybrid setups work best in this case: AI handles speed and scale, and humans are left with handling ambiguity, nuance, and high-risk decisions. Human review remains unbeatable for marketing, legal, medical, and customer-facing content. Use a translation management system to enforce glossaries, translation memory, review workflows, automated checks, and report in one controlled process.
Quality control with POEditor
POEditor gives you access to the Quality Evaluation feature on all plans. It’s based on the MQM framework, and identifies issues by category and severity. Each translation receives a score out of 100.
When issues are found, the evaluation reports the category (e.g., accuracy), severity (major or minor), and the point deduction for each issue.
From the Quality Evaluation results, you can select Fix with AI to generate a revised translation that addresses the identified issues. You can also customize the prompt used for QE Fix in your LLM integration settings.
Quality Evaluation works together with QA Checks. The latter uses predefined rules to identify technical issues like placeholder mismatches, formatting errors, punctuation differences, and inconsistent glossary usage.
Wrapping up
Quality control in AI-assisted translation remains fundament, as it’s a way of ensuring that the translated content meets the required standards of accuracy, consistency, and usability. AI technologies are still prone to errors such as mistranslations, omissions, or hallucinations. As AI-assisted translation becomes increasingly integrated into professional workflows, QC is evolving alongside it.