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Support teams use machine translation as an umbrella term, but the approach behind a translation affects how terminology is handled and what reviewers need to check. Neural machine translation (NMT) is built for translation; a large language model (LLM) can also translate, but it may add plausible words or phrases that were not in the source. A maintained glossary, clear target locale, and review against the original help teams catch those risks.
This glossary uses practical working explanations rather than claiming a single authoritative taxonomy for AI translation in customer support. The standards and guidance linked below cover terminology management, translation evaluation, and related AI vocabulary—not a complete support-team glossary.
What does NMT mean?
Neural machine translation (NMT) is machine translation based on neural-network methods. Microsoft describes NMT as an approach used by many current translation applications, including Microsoft Translator. Its guidance characterizes NMT systems as designed specifically for translation, in contrast with general-purpose LLMs that can be prompted to translate. This is a distinction in approach, not a guarantee that one system will produce better results for every language pair or support domain.
Machine translation (MT) is the broader term for translation produced by a computer system. It can refer to NMT or an LLM-based translation workflow, among other approaches. Use “MT” when the specific technology does not matter; use “NMT” or “LLM-assisted translation” when it does.
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What is the difference between machine translation and an LLM?
MT describes the activity—translation produced by a computer—while an LLM is one kind of model that can be used to perform it. NMT and LLM-assisted translation differ in their design and may differ in terminology controls, flexibility, processing demands, and review risks. Microsoft’s comparison is vendor guidance, not a universal benchmark across products or language pairs.
| Consideration | NMT | LLM-assisted translation |
|---|---|---|
| Primary design | Microsoft describes NMT as optimized specifically for translation. | An LLM is a general language model that can translate as well as perform other language tasks. |
| Terminology resources | Microsoft says existing glossaries and term bases can be easier to integrate. | Integration may be harder, depending on the implementation. |
| Review focus | Check meaning, omissions, errors, and approved terminology. | Check those issues and whether the output adds material absent from the source. |
| Fit considerations | Language pair, domain, customization, and terminology controls. | Language pair, task flexibility, cost, latency, and human review. |
Performance depends on the language, domain, model, and workflow. Microsoft also notes that specialized terminology can be a weak point for LLM translation and that LLMs may fabricate content. These are reasons to assess output and controls in context, not proof that every NMT system outperforms every LLM.
Working glossary for support and localization teams
AI and translation approaches
- Artificial intelligence (AI): A broad field and family of computational systems. Here, it refers to systems used for tasks such as language generation or translation. ITU-T Y Supplement 97 (2025) compiles machine-learning definitions from ITU-T and other standards; it is not a dedicated support-translation glossary.
- Machine translation (MT): Translation produced by a computer system. It may use NMT, an LLM-based workflow, or another approach.
- Neural machine translation (NMT): Machine translation based on neural-network methods. Microsoft says many current translation applications use NMT.
- Large language model (LLM): A general-purpose language model that can be prompted to translate. It is not synonymous with machine translation: it is one possible technology used for that task.
Terminology and locale
- Glossary or term base: A maintained collection of terminology and related information used to support consistent language. The terms are often used for similar resources, though a particular tool may distinguish between them.
- Terminology management / terminography: The work of setting goals for terminology, collecting and researching terms, documenting them, using the resulting resource, and maintaining it. ISO 12616-1:2021 addresses fundamentals and recommendations for translation-oriented terminology collections.
- Localization: Adapting content for a target locale, including language variety and context, rather than changing words alone. Microsoft discusses localization alongside translation and notes that language variants can matter; it gives Portugal Portuguese and Brazilian Portuguese as an example where LLMs may have difficulty distinguishing variants. Treat that as Microsoft’s guidance, not a timeless rule for every model.
- Source text / target text: The original content submitted for translation and the resulting translated content, respectively. Comparing the two helps reviewers identify changed meaning, omissions, or additions.
Review and quality
- Machine translation post-editing (MTPE): Human revision of machine-translated text. ISO 5060:2024 includes evaluation of post-edited machine translation output.
- Post-editor: A person who reviews and corrects machine translation. The reviewer’s qualifications and the depth of review should match the intended use and risk. ISO 5060:2024 discusses evaluator qualifications and competence; it does not prescribe a support-team staffing model.
- Translation quality evaluation: Assessment of translation output against defined error categories or other criteria. ISO 5060:2024 describes an analytic approach using error types and penalty points to produce an error score and quality rating.
- Hallucination / fabrication in translation: Content generated by an AI system that does not appear in the source. Microsoft warns that LLMs can introduce words or phrases that sound plausible but are misleading or incorrect.
How do we keep translated support terms consistent?
Maintain approved terminology as an operational resource, not a one-time list. ISO 12616-1:2021 addresses fundamentals for producing sound bilingual or multilingual terminology collections, including goals, collection, research, documentation, use, and maintenance. It does not prescribe the exact workflow below; these steps apply those ideas to support work.
- Define the use and target locale. Record where translations will appear—such as support replies, help content, or account notices—and specify the intended language variety. A locale decision helps avoid treating variants as interchangeable.
- Collect high-impact terms. Start with product names, feature labels, billing and account language, troubleshooting terms, and phrases agents use repeatedly. Include the source term and its approved equivalent for each target language.
- Document context and usage. Note meaning, part of speech where useful, capitalization, prohibited alternatives, and any context that prevents a term from being confused with a similar one. Assign an owner and a process for proposing and approving changes.
- Connect the resource to the translation workflow. Use glossary or term-base controls where the selected system supports them. Microsoft says existing resources can be easier to integrate with NMT; integration with an LLM depends on its implementation.
- Review output against both meaning and terms. Compare the target text with the source, confirm required terms, and look for omissions, changed meaning, and added claims. Fluent wording alone does not establish fidelity.
- Sample results over time and update the resource. Evaluate representative translations, record recurring errors, and revise terminology or workflow controls when patterns emerge. ISO 5060:2024 covers evaluation of human, post-edited machine, and unedited machine translation output, including sampling and evaluator competence.
For translations with legal, safety, billing, identity, or account-access consequences, route content to qualified language review under your organization’s policy. This is practical risk management; the cited standards do not prescribe that particular escalation rule.
How to think about translation quality
A translation can read naturally yet still omit a condition, change a product term, or add an unsupported instruction. Review should therefore compare target text with source text and judge the errors that matter for the use case, rather than treating fluency as proof of accuracy.
ISO 5060:2024 gives guidance on evaluating human translation, post-edited machine translation, and unedited machine translation. Its described analytic method categorizes errors and applies penalty points to arrive at an error score and quality rating. That provides a structured evaluation approach; it does not establish one universal acceptance threshold for every support message or organization.
Choose evaluation criteria that reflect the consequences of the content. A minor style mismatch in a low-risk reply is different from an altered billing condition or account-recovery instruction. Define who reviews each category and what happens when reviewers find a recurring issue.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Standards and adjacent references
- ISO 12616-1:2021 addresses fundamentals and recommendations for translation-oriented terminography and sound bilingual or multilingual terminology collections. The ISO listing describes the standard as covering the process of setting goals, collecting, researching, documenting, using, and maintaining terminology data.
- ISO 5060:2024 gives guidance on evaluating human translation, post-edited machine translation, and unedited machine translation. Its analytic approach uses error types and penalty points to produce an error score and quality rating.
- Microsoft Learn’s translation overview discusses NMT, LLM translation, terminology integration, localization, and the risk of fabricated content. These are Microsoft’s descriptions and guidance, not a cross-vendor performance ranking.
- ITU-T Y Supplement 97 (2025) compiles definitions for machine-learning terminology from ITU-T and other standards.
- NIST’s glossary and ITU’s machine-learning glossary are adjacent references for trustworthy AI and machine-learning terms. The sources listed here do not establish one authoritative glossary dedicated to AI translation for support teams.
Frequently Asked Questions
Does machine translation mean the same thing as neural machine translation?
No. Machine translation is the broad category of computer-produced translation; NMT is one approach within it.
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Can an LLM translate support replies?
Yes. An LLM can be used for translation, but review the result against the source for terminology errors, omissions, and content the model may have added.
Is a glossary the same as a term base?
Both refer to maintained terminology resources, though individual tools may use the labels differently or distinguish their functions.
What is MTPE?
Machine translation post-editing: a person reviews and corrects text produced by machine translation.
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