Quality
Translating rare languages: how we handle Khmer, Mongolian, Sinhala and 150+ others
The long tail of language
There are roughly 7,000 languages in the world. Commercial translation products support perhaps 100 of them with high quality. Below that threshold, quality degrades rapidly — not because the engineers don't care, but because training data is scarce.
For languages with fewer than a million native speakers, or languages that are primarily oral and written in unofficial scripts, the training corpora available to build neural translation models are thin. A model trained on a million sentence pairs in Spanish and a hundred thousand in Sinhala will produce visibly different output quality.
Tier D languages: what makes them hard
Traxlate's tier system groups languages by training data availability and translation difficulty:
Tier A (1× multiplier): Common pairs with large parallel corpora. English-Spanish, English-French, English-German. Modern high-resource languages with decades of parallel Bible texts, EU legislative documents, and Wikipedia.
Tier B (1.5×): Medium pairs. Good coverage but some domain gaps. Russian, Turkish, Vietnamese, Czech.
Tier C (2×): Hard pairs. Smaller corpora, more complex morphology or script. Arabic, Thai, Hebrew, Chinese, Japanese.
Tier D (3×): Extreme pairs. Very low training data, complex script, or significant structural distance from European languages. Khmer, Lao, Burmese, Mongolian, Nepali, Sinhala, Amharic, and many others.
How we close the gap on low-resource pairs
No degraded fallback. Every supported language gets the same professional-grade pipeline. Rare pairs are not routed to a cheap shortcut just because the volume math doesn't justify the work.
Faithfulness checks, tightened for rare pairs. Every translation is checked for meaning fidelity to the source. For low-resource pairs we tighten the threshold — we flag segments more aggressively for review because the cost of a silent mistranslation is higher and the signals are noisier.
Independent accuracy verification on every segment. Every translation is checked against the source and anything that drifts is held for review before delivery. For rare-language legal and official documents, those flagged segments are where you focus your review.
Human polish for critical documents. No machine translation system — ours included — is production-ready for high-stakes legal or official documents in extreme low-resource pairs without human review. We are honest about this. The machine provides a clean first draft; a professional linguist corrects it before delivery.
Script handling
Traxlate handles all Unicode scripts natively: right-to-left Arabic, Hebrew, Persian, and Urdu; complex Indic scripts (Devanagari, Bengali, Tamil, Sinhala, Khmer, Burmese, Tibetan); CJK ideographs; and minority scripts including Ethiopic (Amharic), Mongolian traditional, and Georgian.
For document reconstruction, RTL languages are rebuilt with correct bidirectional text embedding. Mixed-direction content (Arabic document with embedded English phrases, for example) is handled using the Unicode Bidirectional Algorithm.
Practical guidance
For Tier D languages, the polish pass and the per-segment accuracy check do the heavy lifting — the platform tightens its thresholds and flags more aggressively because the cost of a silent mistranslation in a rare language is higher.
For documents where correctness is required — immigration filings, court transcripts, medical reports — add a human polish pass regardless of language tier. This is not a limitation of Traxlate specifically; it is the current state of machine translation quality for any professional use case with legal consequences.
We are investing in improving our low-resource language models. Each month, our internal benchmark scores for Tier D pairs improve as we add training data and refine post-processing. The gap between high-resource and low-resource quality is narrowing, but it has not closed.