MT Software Reviews Miss a Critical Language Gap

The Comparison Chart Nobody Questions

Every MT software roundup follows the same script: benchmark DeepL against Google Translate, note that Microsoft Translator has a "no-trace" privacy policy, mention that ModernMT adapts in real time, and wrap up with a tidy comparison table. Crowdin's recent breakdown of the top MT engines is a good example — thorough, honest, and genuinely useful for localization managers working with Spanish, French, German, or Japanese.

But that table contains a number that should stop anyone working in US healthcare or government cold: Google Translate supports 249 languages; DeepL supports 30; Amazon Translate supports 75. None of them support Chuukese. None support Pohnpeian. And those aren't obscure academic edge cases — they are the primary languages of tens of thousands of Pacific Islander patients and students living in US states and territories right now, with full Title VI and Affordable Care Act language access rights.

The MT market is projected to hit $4.8 billion by 2032. Not one dollar of that growth is building toward a production-ready Chuukese engine.

Why These Languages Break Every MT Engine

The standard explanation for poor MT performance is "not enough training data." That's true here, but it understates the problem.

Chuukese and Pohnpeian are Micronesian languages with complex morphological structures. A single Chuukese verb can incorporate subject, object, direction, and aspect into one word that has no clean parallel in English. That morphological density is exactly the kind of pattern that neural models learn by processing millions of sentence pairs. For Chuukese, those sentence pairs essentially don't exist in any form that MT developers can scrape.

The result isn't degraded output. It's garbage output that looks plausible. That distinction matters enormously in a clinical setting. A patient reading a machine-translated discharge summary in Chuukese isn't getting a slightly imperfect translation — they may be getting instructions that are meaningfully wrong, with no visual signal that anything is off.

DeepL's much-cited ability to "retain tone and technical specificity" and reduce post-editing time by 90% is real — for the 30 languages it actually supports. For Chuukese, DeepL returns nothing. Google Translate produces output that fluent Chuukese speakers consistently flag as incomprehensible. We have tested this directly.

The Languages Most Affected

To understand the scope, consider who is actually being underserved:

These communities are Compact of Free Association migrants with full legal presence in the US. Their children sit in US public school classrooms. Their parents use US emergency rooms. Both contexts trigger mandatory language access obligations under federal law.

What the Hybrid Path Actually Looks Like

The honest answer is that no MT engine fills this gap today, and likely won't for years. That means organizations working with Chuukese or Pohnpeian populations need a different architecture entirely — one where human translators carry the full load, supported by tools that were built for human-centered workflows rather than automated throughput.

In practice, this means:

Qualified human translators as the foundation. There is no shortcut. For healthcare and legal content in Chuukese or Pohnpeian, a credentialed human translator is not a nice-to-have — it's the only defensible option under Title VI.

Agentic AI for surrounding tasks, not core translation. AI tools can still add real value: formatting source documents, flagging terminology inconsistencies against a human-built glossary, managing project workflows, and running quality checks on completeness. The AI is doing the work that doesn't require linguistic competence in the target language.

Translation memory built by humans. Over time, a well-maintained Chuukese or Pohnpeian TM becomes a genuine asset. It won't power MT, but it reduces repetition costs and enforces terminology consistency across a health system or school district.

Interpreter coverage for real-time needs. For appointments, IEP meetings, and emergency care, qualified telephonic or in-person interpreters remain the only compliant solution. MT-based interpretation tools carry the same zero-coverage problem in production.

What This Means for Healthcare and School District Buyers

If you're a language access coordinator at a health system with a significant Micronesian patient population, the MT comparison charts are not useful to you. What matters is whether your vendor has credentialed Chuukese and Pohnpeian translators with healthcare subject matter experience — and can demonstrate that their output has been reviewed by a qualified second linguist.

"We use AI-assisted translation" is a red flag in this context, not a selling point. Ask specifically: which AI engine are you using for Chuukese? If the vendor can answer that question with anything other than "we don't use MT for Chuukese," walk away.

For language service companies that subcontract rare Pacific pairs, the same standard applies. The MT post-editing model that works efficiently for French or Korean does not transfer to Chuukese. Pricing and timelines need to reflect actual human translation costs, not MTPE rates.

The Honest Takeaway

MT software has gotten genuinely impressive for the languages it was built on. The Crowdin comparison shows real differences between engines that matter for most localization programs. Use those tools where they work.

But build your language access program around the assumption that for Chuukese and Pohnpeian specifically, you are in a pre-MT world. The technology does not exist yet. Human expertise, qualified interpreters, and carefully maintained translation memory are your only compliant options — and pretending otherwise puts real patients and students at risk.

If you're evaluating vendors for Pacific Islander language access and want a direct conversation about what a compliant workflow actually requires, we're straightforward about what we can and can't do.