Overnight projects & "Secondary Pipes": Real-World Rescues by Logrus IT AI QA/Improvement Tools, P.1
The devil really is in the details—and today, you can take a peek at some samples from the Logrus IT AI tools kitchen.
In my recent episodes on AI in translation, I mentioned that the idea of "pure human" translation has largely become an illusion today. At Logrus IT, we’ve moved toward the PRISM process (Pre-Translation, Raw MT, AI-based Improvement, human Specialist review, and Metric-based, hybrid QA).
But nice acronyms only go so far. Let’s look at some tangible proof of how our AI-based Quality Assurance (AiLQA) actually works on real client projects. (Special thanks to our AI trailblazer, Fedir Bezrukov, for collecting and providing these!).
🌙 The Midnight Oil & The Near-Flop. Picture a super-urgent overnight translation. A client hands over presentation decks for a major event after 7 p.m. the night before. Translators are in panic mode, pulling an all-nighter with zero time for traditional editing or proofreading.
One deck featured slides with over two dozen employee photos and names. Two contributors had the same last names (!) and also similar first names: Vera and Vicky. "Vera" was already in the Translation Memory (TM), but "Vicky" was a first-timer. The tiny 3-letter difference resulted in a high fuzzy TM match, easily slipping past an exhausted translator.
Traditional automated QA wouldn't have caught this. Presenting the same name twice with different photos and roles to a client? A relationship killer. AiLQA saved the day: it didn't just flag the mistranslation, it even noted that "Vicky" is a diminutive and suggested using her full name, Victoria, for the formal presentation.
🔄 The "Adequate Translation" Trap. It is incredibly easy to fall into the trap of turning a senseless source text into an equally senseless target without overthinking it.
Take this string from a hardware maintenance manual: "Tools: multimeter (measurable secondary pipe)." A human translator (or regular MT) translated this literally as "measurement on the secondary side of the circuit." Grammatically plausible, but engineering nonsense.
Our AiLQA tool flagged it immediately by analyzing the broader context of testing rectifier bridges. It spotted a classic "Chinglish" artifact: the AI recognized that "secondary pipe" was a literal, erroneous translation of the Chinese word for "diode" (二极管).
Instead of just pointing out a mismatch, the tool explained the root cause of the error and provided a flawless, technically accurate suggestion: "Tools: multimeter (with a diode testing function)" and an equally flawless translation.
This isn't just proofreading; it’s a paradigm shift in localization quality assurance. Our AI tool demonstrates a deep understanding of both context and subject matter area (electrical engineering), bridging the gap between flawed source material and perfect localization.
(To be continued in Part 2).
Timing:
00:00 Intro – AI in translation summary
01:48 The Midnight Oil and the near-flop (#1)
04:48 Of the "Adequate Translation" trap and the “secondary pipe” (#2)
05:15 The legend of the “Adequate Translation” approach
06:15 Of the "Adequate Translation" trap (continued)
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