Low-Resource Languages

Rosetta was built for this. When no off-the-shelf API supports your target language, you build a translation method from scratch — grammar rules, dictionaries, and morphological coaching — and deploy it as a plugin.

This isn't theoretical. i18n-rosetta was born from translating a production website into Plains Cree, where Google Translate returns nothing useful and no commercial API exists.

The Problem

Google Translate supports ~130 languages. There are over 7,000. For the thousands of languages without API coverage, you need:

1. Linguistic knowledge — grammar rules, morphology patterns, orthographic conventions
2. Terminology — domain-specific dictionaries (what does "dashboard" mean in Plains Cree?)
3. Quality benchmarks — how do you know the translations are any good?

The Solution: Coached Translation

Rosetta's llm-coached method injects grammar rules, term dictionaries, and style notes into every LLM prompt. Modern LLMs have surprising capacity for low-resource languages when given the right context.

Step 1: Create Coaching Data

Create a coaching file for your target locale:

.rosetta/coaching/crk.json

{
  "grammar_rules": [
    "Plains Cree is polysynthetic — a single word can express what English needs a full sentence for",
    "Animate/inanimate noun distinction affects verb conjugation",
    "Use SRO (Standard Roman Orthography) unless script converter handles conversion"
  ],
  "dictionary": {
    "home": "kīwēwin",
    "settings": "isi-nākatohkēwin",
    "search": "nānātawāpahtam",
    "welcome": "tānisi"
  },
  "style_notes": "Use formal register. Preserve English technical terms in parentheses when no Cree equivalent exists."
}

Step 2: Configure the Pair

i18n-rosetta.config.json

{
  "version": 3,
  "pairs": {
    "en:crk": {
      "method": "llm-coached",
      "model": "google/gemini-2.5-pro",
      "batchSize": 5
    }
  },
  "languages": {
    "crk": {
      "name": "Plains Cree",
      "register": "SRO syllabics with grammatical precision.",
      "script": "cans",
      "maxRetries": 5
    }
  }
}

Key settings:

• batchSize: 5 — Smaller batches give the LLM more attention per key
• maxRetries: 5 — More generous retry budget for difficult translations
• script: "cans" — Triggers script validation (rejects Latin-only output for Cree)

Step 3: Sync

npx i18n-rosetta sync

The quality gate will validate each translation:

• Rejects source echoes (model just returned the English)
• Rejects wrong-script output (Latin text for a Syllabics locale)
• Rejects hallucination loops (repeated trigrams)
• Retries failed batches with progressively smaller batch sizes

Step 4: Script Conversion (Optional)

If your language uses a non-Latin script but the LLM outputs in Latin transliteration, Rosetta can run a deterministic script converter after translation:

Plains Cree SRO → Syllabics
Serbian Latin → Cyrillic

Script converters run automatically in the sync pipeline for locales with registered converters. They're deterministic (no LLM involved) and cost nothing.

The Eval Harness

For serious method development, the companion MT Eval Harness provides:

• Benchmarking — chrF++, BLEU, exact-match scoring against reference translations
• A/B comparison — test different models, prompts, and coaching data side by side
• Plugin export — package a validated method as a rosetta plugin with benchmarks

The workflow:

Quality Tiers

When you run i18n-rosetta status, each pair shows its quality tier:

Tier	Method Type	Description
standard	llm	Direct LLM prompting, no post-processing
high	llm-coached	LLM + grammar/dictionary coaching
research	fst-gated	LLM + deterministic morphological gate
verified	human-review	LLM draft flagged for human review

Plugins include benchmark scores that are displayed alongside the quality tier.