12 articles in this category
A blunt, step-by-step guide to using machine translation in localization: when to choose MT, how to post-edit, and which quality gates actually matter...
I unpack whether human translators remain essential for localization, using industry data, standards, and the limits of neural machine translation to ...
BLEU scores mislead localization teams. Human evaluation and post-editing standards like ISO 18587 should drive quality decisions, not automated metri...
Choosing between CAT tools and raw machine translation? Here's a practical breakdown of speed, quality, cost, and control—and when each approach makes...
BLEU scores have dominated MT evaluation for two decades, but they lie about quality. For localization, Translation Edit Rate and post-editing effort ...
BLEU is outdated for localization QA. Post-editing effort and neural metrics like COMET and BLEURT correlate better with human judgment and reflect re...
Machine translation has improved dramatically, but relying on it blindly for product localization is a mistake. Learn when to trust NMT, when to post-...
Stop treating machine translation as a one-click answer. In real localization, MT is a raw material. Here's how a working team decides when to trust i...
Think machine translation is ready for prime time? It's not. Here's how to build a post-editing workflow that actually works, with real numbers and a ...
Forget the BLEU score. If you're localizing, the real metric is post-editing effort. Here's how to set up a workflow that actually measures what matte...
If you're post-editing machine translation and not feeding corrections back, you're guessing. Here's a practical, five-step walkthrough to build a QA ...
Localization isn't translation. It's post-editing MT output under ISO 18587. Here's why ignoring that standard costs you quality and trust.