Linigu Blog
Professional articles on translation, CAT tools, and language technology
欢迎来到 Linigu 博客
Linigu 博客是为专业译员、本地化专家和语言技术从业者打造的知识平台。我们分享关于 SDL Trados Studio 等 CAT 工具、文件格式、术语管理以及现代翻译流程的实用文章。
linigu.cloud 致力于解决译员在实际工作中遇到的问题。博客内容介绍如何分析复杂的 SDL 文件，以及如何使用 Linigu SDL Studio Converter 将其快速转换为 Word 或 Excel 格式。
我们的目标是帮助译员提高工作效率、提升翻译质量，并更好地理解翻译技术。
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.