Machine translation and CAT tools.
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.
Think machine translation is ready to replace human translators? Think again. We debunk common myths and explain why CAT tools still matter.
I unpack whether human translators remain essential for localization, using industry data, standards, and the limits of neural machine translation to ...
Think machine translation alone will do? It won't. Here's why CAT tools and post-editing are non-negotiable for quality translations that actually mee...
A working translator's take on why machine translation post-editing is a bad deal for fiction—and when it might, maybe, be okay.
Forget the academic jargon. Here's how translation theory can help you pick tools, avoid disasters, and make better decisions—based on real-world expe...
BLEU is still everywhere, but it's a poor stand-in for human quality. Newer metrics like COMET and BLEURT are better—and here's why you should trust t...
BLEU scores mislead localization teams. Human evaluation and post-editing standards like ISO 18587 should drive quality decisions, not automated metri...
Neural MT slashes costs. But for legal, medical, or marketing text, raw machine output is a liability. A head-to-head comparison of human, MT, and hyb...
Comparing DeepL, Google Translate, and Smartcat for professional translation: quality, workflow, and control. My verdict might surprise you.
We compare BLEU, TER, and COMET for judging MT output in a working translation workflow, then argue that COMET and HTER together offer the most practi...
Stop asking if machines will replace literary translators. The real question is what human translators must do now to survive—and thrive—in the age of...
Machine translation has come far, but literary translation still demands human creativity. Here's why you need a human post-editor and how to do it ri...