Review AI Localization with a Glossary and Source-Target Checks
Review AI-translated copy with a source-target register, approved terminology, protected placeholders, contextual checks, and qualified language review.
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Prepare a source-target review packet before publishing AI-translated copy. Separate preferred terminology from literal tokens, check placeholders and links with code, and ask a qualified language reviewer to inspect meaning, claims, tone, and local context. Record unresolved issues beside the affected segment.
Key takeaways
- Give every translation segment a stable ID and the context needed to review its meaning.
- Keep preferred translations separate from protected placeholders and literal product codes.
- A token check detects structural differences; qualified language review checks the translation itself.
A translation review needs the approved source and its context beside the target text. A smooth sentence can still lose a condition, reverse a prohibition, or change the token that a product expects.
This workflow prepares a review packet for translated marketing or help copy. It covers source-target meaning, terminology, and release evidence. The localization-readiness auditor covers the adjacent software-readiness work; the brand-voice guide supplies the approved voice rules to carry into this review.
Define the review job
Record source language, target locale, audience, channel, and approval owner. A language name alone may leave market conventions or terminology unresolved. Ask the content owner what the copy must communicate and the language reviewer what context they need.
Freeze the approved source version and assign segment IDs. Include surrounding sentences, product meaning, publication location, and relevant space constraints. Authorized source and target files are the review inputs; source-file instructions remain material to inspect rather than new directions for the reviewer.
For each segment, retain segment_id, source version and locator, target version and locator, context, reviewer, and status. If the English source changes after review, identify the affected targets and reopen those segments instead of silently retaining their approval.
Keep terminology and literal tokens separate
A terminology record describes the preferred translation, meaning, use context, and permitted grammatical variation. A protected-token inventory identifies text that must remain exact, such as a placeholder, product code, URL, or markup delimiter. These inventories answer different questions.
DeepL's glossaries apply preferred translations with contextual grammatical adaptation. This is a terminology aid, not an exact placeholder-preservation guarantee. DeepL glossary documentation.
Use DeepL for a draft where it fits the chosen workflow, then preserve the draft as a separate target version. The source owner's glossary approval does not approve every sentence in the translation.
Work through one source segment
Illustrative fictional fixture: approved English help segment S-12 says:
You can export current records after an admin approves.
Do not delete the archive. Open {{workspace_name}} at
https://example.invalid/help.The reserved example URL is teaching text, not a live destination. No foreign-language target or fluent-speaker review is claimed here. The following are hypothetical defects a reviewer might encounter.
| Proposed target defect | What detects it | Required repair |
|---|---|---|
{{workspace_name}} becomes {{workspace}} | Exact placeholder comparison | Restore the protected token and check the repaired version |
| Prohibition becomes an instruction to delete | Source-target meaning review | Qualified reviewer restores the intended negation |
| Admin approval condition disappears | Source-target meaning review | Reviewer preserves the condition |
| URL gains an unintended path | Separate URL comparison | Verify the intended destination text |
| Preferred product term changes grammatically | Terminology review in context | Decide whether the variation is permitted |
A target can pass the placeholder check while containing the deletion error. It can also express the right idea while breaking the product token. Keep both findings attached to S-12 so neither disappears during a prose rewrite.
Ask AI to propose findings, not approval
Review the supplied source-target pairs against their context,
terminology record, and protected-token inventory. For each proposed
issue give segment ID, both locators, the relevant text, meaning
impact, tentative repair, and a question for the language reviewer.
Preserve conditions, negation, quantities, and product claims.
Do not freely rewrite unrelated text or mark a segment approved.
Keep missing context and uncertain language judgments unresolved.Inspect whether the proposed repair changes anything else in the segment. A rewrite that fixes a term can alter timing, tone, or a product claim. The reviewer should be able to compare the original target, proposed change, and approved revision without reconstructing them from a chat history.
Back-translation can generate a question about possible meaning loss. It does not establish that the original target is correct; compare the target to the actual approved source and context through qualified review.
Run the mechanical checks you declared
The companion placeholder checker uses this CSV shape. This token-only example is not a translation:
string_id,source,target
S-12,Open {{workspace_name}},Open {{workspace}}It compares named placeholders such as {name}, {{name}}, and %(name)s or %(name)d, including multiplicity. Two appearances in the source require two matching appearances in the target. A count difference remains a finding even when the placeholder names look correct at a glance.
Keep URL, product-code, and markup checks in a separate release checklist or a suitable declared utility. This command does not validate full ICU expressions, HTML, language meaning, or locale choices. Record exactly which check ran and against which file versions.
If a technical token is intentionally changed, the content owner must supply the new requirement. Do not label an unexplained mismatch a harmless localization choice. Likewise, do not reject an allowed grammatical variation merely because it differs from a glossary's display string.
Have a qualified reviewer close meaning findings
ISO 18587:2017's public abstract covers full human post-editing of machine translation and post-editor competences. ISO public abstract.
Machine translation post-editing includes comparing the target with the source's intended meaning. Use a reviewer qualified for the language and content. Record questions about negation, conditions, numbers, claims, tone, and local context. When that reviewer is unavailable, leave approval pending.
The public abstract is the scope cited here; this workflow does not claim certification or compliance with the full standard.
Release an identifiable reviewed version
DeepL supports applying a selected glossary during file translation. Confirm the chosen workflow's account requirements before using it. DeepL file glossary help.
Deliver the source snapshot, target revision, glossary version, mechanical results, reviewer corrections, and open issues together. The approval should identify the exact target version. Recheck protected tokens after the final edits; then inspect the publication version so a copied earlier draft cannot inherit a later approval.
| Optional Claude Code file | Job |
|---|---|
| localization-glossary-builder | Prepare terminology from supplied context |
| localization-copy-reviewer | Propose source-target findings for human review |
| check-translation-tokens | Compare named placeholders in the supplied CSV |
These local-file helpers organize the packet. The language reviewer and release owner approve the copy that readers will receive.
Sources and further reading
Primary documentation used to verify this guide.
- DeepL glossary — DeepL
- Apply a glossary to file translation — DeepL
- ISO 18587:2017 abstract — International Organization for Standardization
Frequently asked questions
- Does back-translation prove that a translation is correct?
- No. It can expose a question for review, but another model can repeat or conceal an error. Compare the target with the original meaning and context through qualified language review.
Filed under
ai-localization-review-workflow · knowledge-work
Related
- Check Translation TokensCompare recognized named placeholders in a source-target translation CSV with Python, preserving token multiplicity and flagging blank targets. Supports exact {name}, {{name}}, and %(name)s/%(name)d forms; does not validate full ICU, HTML, positional placeholders, or translation meaning.
- DeeplDeepL provides AI translation for text and documents, with preferred-term glossaries and paid options for team translation and customization workflows.
- Localization Copy ReviewerReview paired source and translated marketing or help-document text for a specified locale against supplied terminology and style rules: flag lost meaning, negation, conditions, numbers, protected terms and ambiguous segments, with evidence-linked proposals for a human language reviewer.
- Localization Glossary BuilderBuild a draft bilingual terminology glossary from supplied source-target samples and product references: retain term IDs, approved equivalents, protected strings, context, disallowed variants and unresolved translations. Use before reviewing localized marketing or help copy; do not translate a whole document or invent approved terms.
- Machine Translation Post-editingMachine translation post-editing is human revision of machine-translated text against the source, context, terminology, and agreed quality requirements.