Linigu Blog
Professional articles on translation, CAT tools, and language technology
Bienvenue sur le blog Linigu
Le blog Linigu est une plateforme dédiée aux traducteurs professionnels, aux spécialistes de la localisation et aux passionnés de technologies linguistiques. Vous y trouverez des articles pratiques sur les outils de TAO comme SDL Trados Studio, les formats de fichiers, la gestion terminologique et les flux de travail modernes.
linigu.cloud a été créé pour répondre aux besoins réels des traducteurs. Nos contenus expliquent comment analyser des fichiers SDL complexes et comment utiliser le Linigu SDL Studio Converter pour convertir rapidement ces fichiers en formats Word ou Excel.
Ce blog a pour objectif de vous aider à travailler plus efficacement, à améliorer la qualité de vos traductions et à mieux comprendre les outils que vous utilisez au quotidien.
Proofreading a human translation and post-editing a machine translation may seem similar, but they require different skills, mindsets, and risk awareness. This article explains why.
Using large language models like ChatGPT or Claude to translate entire PDF documents might seem convenient and cost-effective, but this approach introduces serious consistency problems that professional translators and organizations must understand. When the same document is translated multiple times by AI, terminology choices, tone, and sentence structures vary unpredictably—creating quality issues that undermine translation reliability and brand consistency.
Integrating DeepL machine translation with SDL Trados Studio represents one of the most powerful combinations in modern translation technology. DeepL's superior neural machine translation quality paired with Trados's robust translation memory and terminology management creates workflows that dramatically accelerate translation. However, this integration introduces specific challenges, particularly around terminology consistency and quality control, that translators and project managers must unde
Machine translation has evolved from a curiosity to a practical tool used across industries worldwide. Modern neural machine translation systems produce remarkably fluent output, yet they also exhibit predictable weaknesses that limit their application. Understanding where MT excels and where it struggles enables organizations to deploy it effectively while recognizing when human expertise remains essential.