Mirela
Your translation memory holds all those hard-earned translations from past projects, and without regular upkeep, it might get cloged up fast. Cleaning, deduplication, and optimization fix this mess. We’ll walk through the process of translation memory maintenance and the practical steps to get your TM running smoothly again.
The cleaning part
In order to clean a translation memory, you need to evaluate existing segments against current linguistic, terminological, and organizational standards. Every segment in a TM implicitly claims authority: when surfaced as a match, it signals to the translator that this solution has been used before and is therefore reliable. Cleaning is the process of ensuring that this claim remains valid.
Cleaning involves reviewing and removing or correcting problematic entries. For example:
- Obsolete product names.
- Incorrect terminology.
- Segments with formatting errors.
- Incomplete or fragmented sentences.
- Segments imported from low-quality sources.
Remove obsolete and legacy content
The worst TM clutter is outdated stuff. Companies change the product names, features vanish, legal terms get updated, and so on, but TMs hang onto old words forever unless you step in. Stale entries keep popping up as “matches” way past their prime, and that’s a risk because your translators might grab bad terms. Time to clean up: stash old content by version, add expiration tags, or downgrade legacy matches. Keep history for reference, but let only fresh bits steer today’s work.
Correct formatting and tag errors
Don’t overlook formatting and tag glitches! They’re sneaky and don’t scream “error” right away. These inline tags hold variables, placeholders, markup, and layout bits that translators must copy exactly. If you mess them up, you might get busted exports or sneaky bugs in apps and docs. TM scrubbing is key. Tools spot wonky tags or funky patterns, but fixes belong in the memory itself. A spotless TM delivers solid matches.
Filter low-quality segments
Another step in the cleanup phase is to remove the low-quality bits like unchecked machine drafts or off-domain linguist work that ignores the style guide. These segments may not be objectively wrong, but they may fall below current quality expectations.
The deduplication part
Language repeats, so duplicates will appear naturally. Same source text spans projects, docs, and years, and the real issue here is that variation sneaks in with those repeats. Some duplicates are benign, but others are harmful: identical source segments paired with different translations.
Conflicting duplicates are dangerous
Your TM has multiple versions of the same sentence? That’s bad. It forces you to pause and play detective, trying to figure out which option is the right one. There are several consequences to this: productivity decreases, cognitive effort increases, consistency becomes dependent on individual judgment.
Automated deduplication
Most CAT tools have automated deduplication functions that identify identical source segments and apply predefined rules to resolve conflicts. However, there’s a catch. These tools are strictly “logic-based,” not “meaning-based.” They might be programmed to keep the newest entry or the longest string, but being the most recent doesn’t mean a translation is actually right.
Editorial review
When it comes to the high-stakes stuff, you really can’t leave conflicting duplicates up to a machine. You have to clean these clashes manually. Take the time to manually scrub the duplicates. If the same conflicts keep popping up, it’s usually a red flag that your style guide is too vague, your glossary is weak, or there’s something wrong with your review process. You have to fix the root of the problem.
The optimization part
Optimization is all about shaping the translation memory to maximize performance and contextual relevance. A TM can be technically clean and stil perform poorly if it’s not structured and configured correctly.
You might want to separate your TMs by domain or product whenever possible; storing all content types in one TM reduces match quality. And speaking of match quality, it can be refined with metadata. Proper tagging turns the TM into a manageable system.
Large TMs can slow down over time, but regular reindexing and compacting improve search speed and reduce file size. Don’t worry, these technical steps do not change content, they just keep the performance stable.
Translation memory maintenance frequency
How often should maintenance be performed? If you do it too often, you waste time; if you wait too long, it becomes too expensive to fix. We can divide maintenance into three phases. The first is immediate; it happens before the project is officially closed and merged into the Master TM. You start by removing broken tags and fixing typos identified during final review.
On a quarterly basis, you shift your focus to deduplication and health checks. It’s a mid-level audit where you remove redundant translations for identical source strings or segments containing only numbers and punctuation. Annually, the TM should undergo a deeper optimization to ensure it remains a high-value asset. You prune “legacy” data and perform global updates to align the TM with current brand terminology.
Wrapping up
Bottom line: treat TM maintenance as routine care. Run a cleanup on your main database, track improvements in match rates and project speed, then roll it out across the board. You end up with fewer headaches and translations that stay true to your brand.