How should the quality of an AI game-localization system be evaluated?
Evaluate an AI localization system with a blind test of representative, risk-stratified game content. Do not judge a fluent sentence or apply a generic “ready to publish” percentage. Measure critical mistranslation, terminology, character voice, context, variables and markup, interface length, and the time a native-language editor needs to correct it. Gameplay, payment, and compliance errors need their own hard limits.
Game strings are often separated from their context. “Ready” may describe a character state, a button, or a combat message; pronouns, formality, gender, faction, and previous plot may live in another table. Fluent output can still be wrong, and a strong automatic score does not prove that a player understands an objective. The evaluation package therefore retains scene, speaker, adjacent dialogue, character limit, placeholders, and screenshots.
| Content | Main risk | Evaluation emphasis | Default handling |
|---|---|---|---|
| UI, buttons, system messages | Overflow or incorrect action | Length, terminology, usability, variable integrity | Automated checks plus in-context review |
| Skills, numbers, and purchases | Wrong condition, amount, or negation | Accuracy and critical-error count | Mandatory human approval |
| Story and dialogue | Broken voice, continuity, or reference | Style, coherence, and cultural adaptation | Native editor with character and scene context |
| Items, maps, and lore | Inconsistent names and relationships | Term coverage and cross-version consistency | Governed glossary with change history |
| Wordplay, verse, and sensitive material | Literal translation fails or creates local risk | Transcreation and regional suitability | Senior localization rewrite and approval |
When decomposing features, data, and acceptance scenarios, also compare How should a business choose between custom software and SaaS?; the linked guidance adds context that should be considered in the same decision.
Sample for risk, not an arbitrary sentence count
Stratify content by type, character, chapter, length, and impact. Deliberately include placeholders, rich-text tags, ambiguous wording, cultural sensitivities, missing context, and legacy translations. There is no universal 500- or 1,000-line threshold: a small, high-risk paid game may require full review, while a large live game can sample lower-risk content and inspect critical classes completely. Preserve selection rules so a vendor cannot submit only easy strings.
Prepare a glossary, character bible, and style guide approved by native specialists, without treating one reference translation as the only valid wording. Multiple translations can be good. An error taxonomy such as MQM can classify accuracy, terminology, linguistic convention, style, and locale fit while assigning higher severity to errors that change player action or purchase meaning. The W3C MQM Community Group develops this analytical approach for human, machine, and generative-AI translation.
Measure dependable editorial savings, not a model score
Compare a human baseline, a general model, and a terminology-and-context-assisted system on the same sample. Hide the source and randomize candidates. Have two editors independently label a calibration subset; large disagreement means the rubric needs repair. Record editing minutes, changed characters, critical errors, and full retranslations per hundred strings or comparable text unit. That evidence shows whether the system reduces reliable work.
Automated checks are valuable for placeholders, HTML or rich-text tags, numbers, currencies, line breaks, banned terms, whitespace, and length. They do not judge language. A glossary match can itself be grammatically wrong. Before release, inspect real screens for truncation, fonts, direction, and voice/subtitle synchronization. Regression-test updates so an old translation memory does not contaminate a new story.
Acceptance can require zero critical errors in high-risk content, agreed placeholder integrity, bounded terminology errors, lower native-editing time than the human baseline, and no blocking issues in player tests. Record sample, language pair, model and prompt version, settings, and editorial rules. Retest model upgrades instead of extrapolating one result indefinitely.
Wavesteam starts with a small reproducible evaluation workspace rather than a full platform: import strings and context, fix candidate systems, blind the review, classify errors, and capture editing time. Glossary, translation memory, bulk jobs, permissions, version diff, and publishing integration follow only after evidence of value. Native-language specialists retain final approval; Wavesteam owns the system, traceability, and reproducibility. The AI Chinese learning case is relevant public language-AI experience, not proof that a new language pair will meet its target.