A rigorous 10-stage Chinese-Vietnamese translation workflow that combines terminology management, multi-model reflection, blind back-translation, and human review.
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npx clawhub@latest install back-translate-reflect
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回译反思法 is a specialized translation quality engine for Chinese-Vietnamese localization, based on the 10-stage workflow developed by 悦迅翻译 and 回译派. It combines terminology extraction, web verification, glossary standardization, batch translation, reflective editing, final revision, blind back-translation QA, defect repair, delivery gates, and compliance review.
This Openclaw Skills workflow is designed to make AI more valuable to translators rather than replace professional judgment. Its Vietnamese-focused reinforcement layers target pronoun stance, classifiers, post-nominal modifiers, passive voice, aspect markers, sentence-final particles, cultural terms, Sino-Vietnamese vocabulary, tones, and regional usage.
{glossary:[{src,vi,zh,note}]}.Minimum operational inputs
The skill defines logical translation artifacts rather than a mandatory filesystem layout. Implementations can store each artifact as JSON, Markdown, or records in an orchestration system.
| Artifact | Core fields | Purpose |
|---|---|---|
| Source document | src, language, domain, audience, formatting rules |
Canonical input for every downstream stage |
| Term extraction | terms[] with src, type, hint |
Captures names, entities, abbreviations, technical terms, and cultural terms |
| Verification record | Source term, approved equivalent, source link, confidence, notes | Documents external evidence and translation decisions |
| Glossary | glossary[] with src, vi, zh, note |
Enforces consistent terminology across the translation |
| Draft translation | Source segments, target text, model identifier, stage | Records the initial batch translation produced by Model A |
| Reflection review | Issues, rationale, revised text, checklist dimensions, model identifier | Captures Model B analysis and Vietnamese-language improvements |
| Final translation | Final target text, variants, unresolved items, revision metadata | Represents the text eligible for blind QA |
| Blind back-translation | Back-translated source text, segment comparisons, Model C identifier | Detects semantic loss after final revision |
| Repair report | Defect type, severity, evidence, corrected segment, repair model | Tracks targeted fixes without letting the QA model silently rewrite content |
| Delivery and compliance gate | Human decisions, legal checks, regional checks, formatting status, confirmation items | Records approval, escalation, and release readiness |
Metadata taxonomy
The source does not prescribe generated filenames or directories, so an implementation should preserve stage numbers, model identifiers, source references, glossary versions, and review timestamps in its own storage layer.
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