Translation memory is a database that stores previously translated sentences or phrases and suggests them when similar text appears. This helps translators work faster and more consistently.
But how is it different from machine translation? And where did this technology even come from?
In this guide, we’ll cover the basics, clear up common confusion, and show you how translation keys and translation memory evolved into important tools in modern translation workflows.
🧠 Zero-fluff content
Many translators, content managers, and localization professionals feel frustrated by repeatedly translating similar content. This guide is a simple intro to translation memory technology that will help you learn how to reuse existing translations, regardless of your target language.
Think of translation memory (TM) as your personal translation archive. Every time you translate a sentence, phrase, or paragraph, it stores your translation units automatically. Then, the next time you encounter the same or a similar piece of text, it suggests your previous translation.
In other words, translation memory helps you avoid translating the same thing twice, saves you time, and ensures all your translations stay consistent. Not only can it help you speed up your translation process, but it also ensures consistency across all target languages.
Learn more about it in the video below.
Translation memory helps you reuse your existing translations, while machine translation automatically generates new ones.
Martin Kay, a pioneering linguist and researcher at Xerox PARC, first proposed the idea of translation memory in his paper, “The Proper Place of Men and Machines in Language Translation”.
Believe it or not, the paper was published in 1980, which means that translation memory has been around for more than 40 years. The evolution of it is exciting, so let's take a look.
Translation memory was born from a very practical need–to reduce repetitive translation work. The concept itself emerged in the late 1970s, but it was really brought to life in the early 1980s.
Let’s explore what was Martin Kay’s stance shortly.
Martin Kay, an expert in computational linguistics, photo from the 1980s
Kay believed that professional translators need to work with machines in order to make their work more human:
“Translation is a fine and exacting art, but there is much about it that is mechanical and routine and, if this were given over to a machine, the productivity of the translator would not only be magnified but his work would become more rewarding, more exciting, more human.”
Although he never used the phrase “translation memory”, he did suggest putting a word in a “local store” and reusing the translation where appropriate.
🧠 Did you know?
Early TM solutions were experimental and mainly used by large companies dealing with complex documentation (like tech manuals or automotive instructions).
By the early 1990s, translation memory became more accessible and practical. The first widely adopted commercial TM system, Trados (developed by Jochen Hummel and Iko Knyphausen), launched in Germany.
It transformed translation memory from a theoretical concept into a practical tool professional translators could actually use.
Trados quickly became the industry standard, making translation memory more common among freelance translators, agencies, and global companies.
The internet changed everything, and translation memory is not an exception. New web-based tools emerged. Translators and localization teams could now collaborate and share translation memories more easily. For those exploring “what are translation services“, this evolution highlighted how technology could enhance efficiency and consistency. In 2005, SDL acquired Trados, making TM an integral part of broader localization platforms.
Cloud technology opened up even more possibilities. Translators could now access translation memories anytime, anywhere.
In recent years, translation memory started integrating closely with machine translation (MT), AI-driven quality assurance tools, and content management systems (CMS).
Smart TM tools began automatically suggesting similar segments of text, merging human translations with AI suggestions for even faster workflows.
Today, translation memory is smarter than ever. It streamlines translations, helps with consistency, and significantly reduces costs.
Translation memory uses algorithms and similarity calculations to quickly scan and match new segments with stored translations. This is how it’s able to give you instant suggestions.
It’s basically a database filled with pairs of sentences or segments of already translated text. So, you have the original (source) text and its corresponding translation across different languages.
TM systems works by comparing new text you're translating with segments you’ve already translated and saved in your database. Depending on how closely these new segments match your stored translations, you’ll see one of four common matches.
| Exact match (100%) | If your new segment is identical to a previously translated one, you have a perfect match. In this case, TM systems will instantly suggest your previous translation. |
| Context match (101%) | Your new segment is identical to a previous translation, and the sentences around it (the context) also match. It's even better than an exact match because the context ensures it's the perfect translation for a specific case. |
| Fuzzy match (partial match) | Your new segment is similar (but not identical) to one you've translated before. The translation memory suggests your stored translation along with a percentage (like 85%) to show how similar it is, giving you a helpful starting point. |
| No match | There’s nothing in your translation memory that matches or even closely resembles your new segment, so you’ll have to translate it from scratch. Once you do, it gets saved for next time. |
To better understand how modern TM matching works, see this study on intelligent translation memory matching.
Creating a translation memory is actually pretty simple. Your translation memory software handles most of it automatically.
Here’s how to get started:
Just translate as you normally would, and your translation memory grows and becomes more useful over time.
If you’re looking for an overview of translation memory best practices, check the table below.
| Best practice | What it entails |
| Regular maintenance | Occasionally review and clean up your translation memory to remove outdated translations and update terminology. |
| Be consistent | Stick to clear terminology guidelines or glossaries. The clearer and more consistent your translations, the better your TM works. |
| Segment properly | Keep sentences or segments clear and complete. Shorter, complete segments work better than long or fragmented ones. |
| Backup regularly | Always back up your translation memory regularly, just to make sure you don’t lose valuable work. |
| Collaborate carefully | If multiple translators share the TM, set clear guidelines and processes to maintain consistency across your team. |
💡 Pro tip
New translators joining your team? Create a “starter” translation memory for them. Before new translators begin working, handpick and compile your best translations. Think the ones that truly reflect your brand voice or ideal style.
Then, use these to build a small, custom translation memory. New team members will instantly have great examples to learn from, and they are more likely to adapt to your tone and style.
Translation memory has multiple benefits. It helps you translate faster, reduce translation rates, and keep your content consistent by reusing previously translated text. More specifically, it:
Now let’s see a real-life example.
How translation memory saved 16% of translation costs for ActiveCampaign
ActiveCampaign had a common translation problem: their process relied heavily on machine translation and manual fixes, with little to no translation testing, which meant they were repeatedly translating the same phrases, wasting time, and spending too much money.
Once they started using Lokalise and its translation memory feature, their translations got a lot easier.
They began saving every sentence or phrase they translated into a database (translation memory) organized by different content types (e.g., marketing materials, product descriptions, support articles), which was crucial for ensuring consistency and quality in marketing translation, where adapting content for new audiences is key.
Because of this, whenever similar text came up again, the previous translation popped up instantly, ensuring consistency and supporting translation quality assurance. They didn’t have to translate the same thing twice.
Results were clear:
If you want to explore Lokalise, you can sign up for a free 14-day trial, no credit card required. Bear in mind that Lokalise is more than a translation memory software. It’s an all-in-one platform that helps you translate and localize your content, and successfully collaborate with all stakeholders.Want to continue learning? Explore the Lokalise blog for other useful resources.
Author
Mia has 13+ years of experience in content & growth marketing in B2B SaaS. During her career, she has carried out brand awareness campaigns, led product launches and industry-specific campaigns, and conducted and documented demand generation experiments. She spent years working in the localization and translation industry.
In 2021 & 2024, Mia was selected as one of the judges for the INMA Global Media Awards thanks to her experience in native advertising. She also works as a mentor on GrowthMentor, a learning platform that gathers the world's top 3% of startup and marketing mentors.
Earning a Master's Degree in Comparative Literature helped Mia understand stories and humans better, think unconventionally, and become a really good, one-of-a-kind marketer. In her free time, she loves studying art, reading, travelling, and writing. She is currently finding her way in the EdTech industry.
Mia’s work has been published on Adweek, Forbes, The Next Web, What's New in Publishing, Publishing Executive, State of Digital Publishing, Instrumentl, Netokracija, Lokalise, Pleo.io, and other websites.
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