27 Januar 2026
A practical maintenance checklist for MT glossaries
Machine translation glossaries can’t just be compiled then left to get on with the work. They need upkeep. Whereas term bases have more general uses, MT glossaries comprise lists of word or phrase pairs which are specifically aimed at resolving recurrent errors in machine translation.
The design of an efficient MT glossary should facilitate immediate corrections without the necessity of curating metadata, and performance depends on keeping each term in the source language associated with a single, unambiguous translation.
Effective maintenance of MT glossaries requires continual vigilance. Ideally, entries need to be selected based on observed output issues, and all new possible entries should be tested before inclusion. Glossary items that do not enhance translation quality should be promptly removed to ensure the aimed-for high performance.
Alongside these actions, translator feedback and post-editing corrections can guide ongoing updates. Additionally, regular audits will catch ambiguous terms and unnecessary entries. Another important factor to consider is size: an MT glossary needs to be kept lean as this will help with speed and functionality.
To help with what may now seem like a mammoth task, we’ve broken the process down to explain a few of the most important actions you should take, and why.
This sounds like a mouthful but it’s not as confusing as it first appears. Essentially, you should establish a systematic review process which will help in monitoring your final machine translation output, i.e. after the MT glossary has been applied.
This review process ideally includes analyzing translated content across multiple projects which will help detect patterns of mistranslation, inconsistent terminology, or domain-specific errors that persist despite glossary use. AI-powered quality evaluation tools can help with this.
Importantly, don’t rush the process. Before finalizing any glossary addition, conduct controlled testing to check that the proposed entry actually improves translation quality without unwittingly introducing new problems.
You can create test sets containing the source term in various contexts and sentence structures to ensure the glossary translation will work universally, then run comparative translations with and without the glossary entry to measure impact, and check that any grammatical errors, awkward phrasing, or contextual mismatches haven’t crept in.
At Alpha, our language architects are experts in adjusting MT glossaries to ensure optimal output.
In short, listen to your linguists – they have much wisdom to share. You can help them by creating structured feedback channels for them to any report terminology issues, as well as suggest glossary improvements. Think about implementing feedback forms, annotation tools, or integrated commenting systems within the translation management platforms to make these tasks easier.
And give them the chance to actually voice their opinions with regular meetings or review sessions where they can discuss recurring problems and propose solutions. If you still have time, you can also document the rationale behind linguist-suggested changes to build a knowledge ‘bible’.
Be ruthless: if it doesn’t make the cut – cut it! The glossary should be regularly reviewed to identify terms with multiple possible meanings or context-dependent translations, such as „bank“ (financial institution vs. riverbank). Examine whether glossary entries are too broad or too narrow in scope.
Go at it with a fine-toothed comb: remove or refine entries that force incorrect translations in certain contexts, even if they work well in others, and consider splitting ambiguous entries into separate entries in their own right for better usage.
Yes, we can’t stress the importance of regularly reviewing, auditing, and evaluating the contents of your MT glossary enough. Over time, with, for example, new marketing campaigns, branding initiatives, and product launches, some glossary entries will become obsolete, ineffective, or counterproductive, and they need weeding out and/or replacing.
One idea is to track metrics for each glossary term, including how often it’s triggered, whether it consistently produces correct translations, and whether it requires frequent post-editing corrections. Also remove terms when product names change, when industry terminology evolves, or when the MT engine’s base model improves to the point where the forced translation is no longer necessary.
Need we even say it? You guessed it – size matters. By limiting glossaries to only essential terms that demonstrably improve translation quality will help with the speed and efficacy of your MT glossary. You can get a head start of controlling the size of your glossary by establishing maximum entry limits based on your MT system’s capabilities and performance requirements. As some systems handle hundreds of entries efficiently, while others degrade with more than fifty.
Create separate glossaries for distinct domains or projects rather than maintaining one massive universal glossary. Regularly review glossary size metrics and you can even try the ‘nightclub rule’ – the ‘one in, one out’ policy – when approaching capacity limits.
The above suggestions will hold you in good stead as basic steps to help maintain your MT glossary. As always, there is a lot more you can do – or someone else can do on your behalf.
Let’s explore some real-world examples of effective, well-chosen MT glossary examples and ambiguous, misleading examples. Here we’ll look at instances in French, as found in generic MT glossaries.
“cloud computing”→ “cloud computing”
“dashboard” → “tableau de bord”
Its inclusion resolves MT tendency to leave untranslated or use incorrect alternatives like “panneau de contrôle”
“application” → “demande”
Additionally, it forces incorrect translation in multiple contexts, degrading overall quality
“book” → “livre”
Quite simply: the way to maintain an effective MT glossary is by lavishing it with regular attention and disciplined curation. Think of the pillars of successful glossary maintenance (continuous monitoring of translation output and ruthless removal of ineffective or ambiguous entries) to ensure optimal machine translation performance.
Remember that a smaller, well-curated glossary of 50 highly effective terms will consistently outperform a bloated list of 500 entries that includes outdated, ambiguous, or counterproductive translations.
By establishing systematic review processes and actively testing new entries before implementation, you’ll create a dynamic resource that evolves alongside your translation needs.
About the author
Amelia Morrey is lead copywriter at Alpha CRC. She has worked with clients across multiple industry sectors, from gaming to engineering. During her time at Alpha, she has collaborated with linguists and operations teams in order to bring localization tips and tricks to the world.
