--- title: Post-Editing Has No Place in Your Localization Strategy description: Here are some of the common pitfalls of a legacy machine translation post-editing approach to translation image: https://labs.lilt.com/hubfs/macro-focus-cogwheel-gear-159275%20(1).jpeg --- - [Lilt.com](https://lilt.com/) - [Resources](https://lilt.com/resources) - [Lilt.com](https://lilt.com/) - [Resources](https://lilt.com/resources) ## Post-Editing Has No Place in Your Localization Strategy ![](https://labs.lilt.com/hubfs/Lilt_June2018/Layout/default-80.png) by Lilt Labs August, 23, 2018 2 Minute Read - - - Machine Translation has historically been a dirty pair of words in localization. Experienced language professionals fear their own work, complete with nuanced diction and hyper sensitive geographical considerations, will be replaced by the cold, lifeless, robotic output of an algorithm. And considering recent approaches in translation, they’re not far off. Of late, translators have been tossing huge batches of English language content into a machine translation engine and then “post-editing”, or revising the output, word by word. While organizations will argue this makes translators more efficient, there’s a considerably greater cost to the approach. At best, the process is tedious, and at worst it threatens to turn translation into a joyless, character devoid sweatshop industry. This is problematic not just to the translators who take deserved pride in their work, but also to localization managers who want to present quality material to new markets. If translation work is sweatshoppy, then the best translators aren’t going to want to work on your localization projects. And without the talent, you can forget about bringing your organization to new markets in a meaningful, contextual manner. Beyond the penalty your brand will pay by serving low quality content, the standard MT/post-editing one-two punch presents additional inefficiencies. Firstly, your content doesn’t improve over time via the accumulation of data and translator feedback. When it comes to establishing a consistent voice and increasing efficiency through term bases and translation memory, legacy MT engines often fall short. So translators hate post-editing and, worse, it creates poor content. What’s next? Glad you asked! Neural Machine Technology (NMT) offers to work alongside translators, not as their replacement, but as a personal assistant that can make recommendations in real time based on previous content translated for your business. Don’t just take it from us, though. Translators are [just as thrilled](https://labs.lilt.com/neural-machine-translation-huge-leap-forward?hsLang=en) about the notion. Recommended articles: [The Augmented Translator: How Their Jobs are Changing and What They Think About It](https://labs.lilt.com/the-augmented-translator-how-their-jobs-are-changing-and-what-they-think-about-it?hsLang=en) [Is Post-editing Dead?](https://labs.lilt.com/is-post-editing-dead?hsLang=en) What have your experiences been with MT/post-editing or NMT? Let us know in the comments! [View All Posts](https://labs.lilt.com) ### [![pexels-photo-373543 (1)](https://labs.lilt.com/hubfs/pexels-photo-373543%20(1).jpeg) August, 1, 2018 Here's Why Neural Machine Translation is a Huge Leap Forward 4 Minute Read ![](https://labs.lilt.com/hubfs/0.jpeg) Though machine translation has been around for decades, the most you’ll read about it is the perceived proximity to the mythical “Babel Fish” --an instantaneous personal translation device-- itself ready to replace each and every human translator. The part that gets left out is machine translation's relationship with human translators. For a long time, this relationship was no more complex than post-editing badly translated text, a process most translators find to be a tiresome chore. With the advent of neural machine translation, however, machine translation is not just something that creates more tedious work for translators. It is now a partner to them, making them faster and their output more accurate. Read More](https://labs.lilt.com/neural-machine-translation-huge-leap-forward?hsLang=en) ### [![](https://labs.lilt.com/hubfs/pexels-pixabay-373543.jpg) July, 15, 2021 Artificial Intelligence Language Learning 4 Minute Read ![](https://labs.lilt.com/hs-fs/hubfs/Drew%20Evans_Edited%20Headshots-large-0725%20copy.jpg?width=299&quality=low) How AI Can Speed Up Your Localization Read More](https://labs.lilt.com/machine-learning-language?hsLang=en) - [System Status](https://status.lilt.com/) - [Support Terms](https://lilt.com/terms) - [Privacy](https://lilt.com/privacy) - - - - [Careers](https://lilt.com/careers)  2026 Lilt, Inc.