An AI LQA Scoring Framework: A Reusable Template for Grading Game Translations

Spot-check 5% of the strings, then go by gut feel that "it's broadly fine"—that's the state of LQA on most game teams. The trouble is that everything outside that 5%—the term drift, the overflowing button labels, the honorifics aimed at the wrong character—gets read word for word by your players. To make quality measurable, regressable, and runnable on every build, you don't need more reviewers. You need a scoring framework a machine can execute.

Why Game LQA Can't Just Borrow General Translation Scoring

Mature scoring standards like MQM and DQF were designed for documents, contracts, and web pages. They care whether meaning is accurately conveyed, but their out-of-the-box dimensions don't cover game-specific failure modes like whether a line of dialogue gets truncated when it lands in a UI button, nor whether an NPC should speak in formal or casual register. Game text has a few failure modes all its own:

• Placeholders and variables: {playerName} defeated {bossName}—order, format, and escaping must all be exact.
• Character/pixel constraints: buttons, speech bubbles, and skill names have hard length limits; overflow is a display bug.
• Character register: the translation of a single "yes" should be completely different for an arrogant villain versus a timid villager.
• Culture and compliance: number taboos, religious symbols, and region-sensitive terms are store-approval red lines in certain markets.

General scoring systems can't grade any of this. So the first step in game LQA is to redefine your scoring dimensions around game context—the same starting point we hammer on in the game localization QA checklist.

Five Scoring Dimensions and Suggested Weights

We break measurable quality into five dimensions. The weights aren't law—they're a sensible default that you should tune by content type (more on that below).

Dimension	What it scores	Suggested weight	Hard-fail item?
Accuracy	Is meaning complete; any omissions/mistranslations/factual errors	35%	No
Term consistency	Does it hit the termbase, same rendering throughout	20%	Partial (key terms are hard-fail)
Register & character voice	Honorifics, person, gender, personality—matched to the character profile	20%	No
Format & constraints	Placeholders, tags, length caps, line breaks	15%	Yes
Cultural compliance	Taboo words, region-sensitive content, legal red lines	10%	Yes

Hard-fail items are the key design choice in this framework. A line can score perfectly on accuracy, but if it breaks a placeholder or trips a compliance red line, the total score drops to zero and the string is sent back. Quality isn't an averaging game—a variable that will crash the build can't be "averaged out" by fluent prose.

How a Machine Scores Each Dimension

• Accuracy: have an LLM do a bidirectional back-translation comparison and flag semantic drift; in parallel, use rules to catch omissions (empty target, abnormal length).
• Term consistency: a hybrid of rules and semantics. First do exact/fuzzy matching against the termbase, then let the model judge whether the term should actually apply in context (so you don't false-flag a word that means one thing as a noun and another as a verb).
• Register: feed the character profile (identity, personality, who they're speaking to) to the model and let it judge whether the translation's tone, formal/casual register, and person are self-consistent. This is exactly what a general engine can't do and a game-context-aware engine can.
• Format constraints: pure rules, the most deterministic of all. Compare placeholder sets character by character, compute length by render width, and check tag pairing.
• Cultural compliance: maintain a per-region table of sensitive words and rules, with the model doing a semantic-level pass to backstop the implicit phrasings that plain string matching misses.

Turn Scores Into Actionable Fixes, Not Just a Report

Scoring is only the beginning. Most LQA tools stop at "export an Excel and flag 200 rows red," then hand it back to a localization manager for manual triage. The real time-saver is to keep scoring and fixing inside one loop:

1. Triage: by which dimension was hit, sort issues into P0 (hard fail, blocks release), P1 (key terminology/register errors), and P2 (readability improvements).
2. Cluster: the same term mistranslated 40 times is one issue, not 40. Cluster it, fix once, and back-fill globally.
3. Fix in place: for P2 issues, offer a suggested translation the reviewer accepts in one click; for P0/P1, pin it to the exact string with the failure reason attached.

If the engine is embedded in the game's runtime, this step can go further: when overflow or a placeholder error shows up, you can fix the string live, in-game, without waiting for the next build (when runtime string updates are wired in). We walk through this chain in fixing strings live, in-game—it turns LQA from "a gate before launch" into "a continuous process you can regress anytime."

Make It a Reusable Template: A Three-Layer Structure

To reuse this framework on every project instead of rewriting the prompt each time, freeze it into three layers:

• Dimension layer (global, shared): definitions of the five dimensions, scoring anchors (what counts as a 5, what counts as a 2), and hard-fail rules. Define this once; every project shares it.
• Project layer (one per game): termbase, character profiles, UI length specs, target-market compliance tables. This is the interface that "wires" the general framework onto a specific game.
• Run layer (every build): weight config (switched by content type), sampling ratio, and thresholds (below what score does it auto-reject).

For example, different content types should use different weights:

Content type	Accuracy	Term	Register	Format	Compliance
UI / system copy	25%	25%	5%	40%	5%
Main-quest dialogue	30%	15%	35%	10%	10%
Marketing / store copy	30%	15%	15%	10%	30%

UI copy weights format highest, because it's the most likely to overflow; story dialogue maxes out register, because character voice is the core of immersion; store descriptions push compliance up, because they face regional review head-on.

Set a Credible Baseline for the Scores

The most common objection to machine scoring is: "can you trust a score the AI gave itself?" The answer is—it needs calibration. The method is to periodically take a batch of translations, run both AI scoring and a blind review by a senior reviewer, and look at the correlation. Once the AI tracks closely with humans on accuracy, terminology, and format (the more objective dimensions to begin with), you can concentrate human effort on register and creative adaptation, where the machine is least sure. That brings us back to the same conclusion from the AI vs. traditional translation blind test: let AI carry the measurable correctness work, and reserve people for the parts that genuinely need judgment.

Conclusion

Game LQA has stayed a gut-feel exercise because it lacked a scoring language a machine could run. Break quality into five dimensions—accuracy, terminology, register, format, and compliance—give each a weight and a hard-fail line, then freeze it into a dimension-project-run template, and you've turned "broadly fine" into a score that auto-regresses on every build. Start with the one dimension that hurts most—usually format constraints or term consistency—get it running as rules, then layer in the other four.