Should you still care about translation quality?
Blog post from DeepL
DeepL argues that while standardized translation quality scores remain useful, narrowing differences among leading AI systems mean that single-number rankings often fail to reflect real-world enterprise translation needs. It explains that human evaluation frameworks such as MQM assess accuracy, fluency, terminology, style, and local conventions, whereas automated metrics including BLEU, TER, COMET, and GEMBA can be biased or overly reliant on reference translations and may not align with human judgments. The company contends that sentence-level tests often overlook document-wide context, consistent terminology, brand style, determinism, speed, scalability, and cost, which can have greater business consequences when translation is deployed across products and organizations. DeepL says its human-expert benchmarking found it leading competitors in many tests, but it emphasizes a broader, customer-specific view of quality and presents its Translation Quality Evaluation model as a tool for identifying potential issues while checking translations against organizational glossaries, style rules, and translation memories.
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