How to Review AI-Assisted Translations

Learning English

This guide explains how to review AI-generated translation drafts for meaning, terminology, accessibility, and tone.

Topics: AI-Assisted Translation Governance Translation Quality

Review checklist

check meaning before style
verify terminology against the project glossary
inspect sensitive wording carefully
confirm accessibility text is localized too
mark review maturity honestly

Slovenian review case study

Original AI draft

Skrbniki morajo moči OCR, prevajanje in ocenjevanje stanja po potrebi tudi izklopiti. Plačljive obogatitve morajo biti izrecne, sledljive in stroškovno nadzorovane.

Corrected Slovenian

Skrbniki morajo imeti možnost, da po potrebi izklopijo OCR, prevajanje in ocenjevanje stanja. Plačljive obogatitve morajo biti izrecno omogočene, sledljive in stroškovno nadzorovane.

Human-review feedback

Issue 1

original: morajo moči
corrected: morajo imeti možnost, da
categories: grammar, modality
likely AI error cause: literal translation of an English modal construction
reviewer rationale: the original phrasing is ungrammatical in Slovenian; the corrected version uses the natural policy-language construction.

Issue 2

original: po potrebi tudi izklopiti
corrected: da po potrebi izklopijo
categories: fluency, word order
likely AI error cause: awkward source-language word order and weak discourse control
reviewer rationale: the corrected order is clearer and removes unnecessary emphasis.

Issue 3

original: morajo biti izrecne
corrected: morajo biti izrecno omogočene
categories: terminology, register, domain-policy phrasing
likely AI error cause: dictionary-level wording chosen without enough product-policy context
reviewer rationale: the requirement is about explicit enablement of paid enrichment, not about describing the enrichments as inherently explicit.

Scoring guidance

Use a lightweight 0-3 scale for repeated review work.

0: unacceptable
1: major problems
2: usable with review edits
3: strong or near-publishable

Recommended scoring dimensions:

meaning accuracy
grammar and fluency
terminology and domain fit
register and style
review effort

Release recommendation guidance

blocker: not suitable for publication or user exposure
major_revision: meaning may be close, but substantial rewriting is still needed
minor_revision: usable draft with targeted native-speaker edits
ready_with_review_signoff: suitable once normal reviewer signoff is complete

Document the correction, not only the fix

For reusable review work, capture:

original text
corrected text
error category
root-cause hypothesis
reviewer rationale

Reusable lessons from Slovenian review

The Slovenian case-study pattern is broadly reusable across languages:

grammatical meaning can be close while the sentence is still unpublishable to a native speaker
modality is especially vulnerable to literal translation
terminology and policy wording often need domain-aware rewriting
automated QA is helpful but rarely sufficient on its own for subtle register issues

Localization-at-Scale note

Even when the overall meaning is preserved, AI-generated translations may require native-speaker review to correct subtle issues in grammar, modality, terminology, and domain-specific register. These issues are often difficult to detect through automated quality metrics alone.

Where to reuse examples

reviewer training materials
AI translation evaluation datasets
style-guide examples
human-in-the-loop workflow docs
localization-at-scale articles and best-practice guides

These examples help reviewers calibrate expectations and give localization leads realistic benchmark material.

../wiki/ai-assisted-translation-policy.md
../wiki/translation-quality-assurance.md
../style-guide/localization/ai-translation-review-records.md
../blog/en/ai-will-not-replace-translators.md