PATTERN Cited by 1 source

Reviewer edits as translation learning signal¶

Intent¶

Treat edits made by professional translators during mandatory approval as structured evidence for improving later AI drafts, rather than discarding the difference between draft and approved copy after release.

Shape¶

AI draft → translator approval/edit → approved translation + edit delta
                                        │
                                        ├── Translation Edit Rate by locale
                                        └── model, context, glossary, or style improvement

Why it works¶

The reviewer already performs the highest-value correction in the workflow. Capturing the approved result and its delta concentrates future improvement on real production failures in terminology, tone, grammar, and contextual interpretation. Atlassian describes the loop as a path to progressively lower edit rates and estimates up to 50% savings, while retaining human approval for every release. (Source: sources/2026-08-06-atlassian-scaling-localization-at-atlassian-keeping-translation-at-the-pace-of-ai-era-development)

Guardrails¶

Treat feedback as locale- and product-specific. A correction can encode a local convention rather than a universal rule.
Preserve reviewer attribution and approval auditability.
Separate quality monitoring from automatic release policy: decreasing TER is not proof that the model can bypass review.
Filter sensitive or customer-specific text before using it for model or retrieval improvements.

Seen in¶

sources/2026-08-06-atlassian-scaling-localization-at-atlassian-keeping-translation-at-the-pace-of-ai-era-development — reviewer edits feed future model improvement after professional approval in Smartling.