How NagaTranslate produces a translation
Translation for a low-resource language cannot be done the way translation for French is done, because the data does not exist. Here is what this site does instead, and where it falls short.
The constraint everything else follows from
A conventional machine translation system learns from parallel text — the same content in two languages, in enormous quantity. For English and French there are billions of aligned sentence pairs. For English and Ao there are, generously, a few thousand.
Training a translation model in the usual way on a few thousand pairs produces something unusable. So NagaTranslate does not do that. It uses a large general-purpose language model and supplies it, at the moment of translation, with the specific evidence needed for the sentence in front of it.
What happens when you press Translate
- Your text is analysed for the words and constructions it contains.
- Relevant contributed examples are retrieved from the phrase set for the target language — existing sentence pairs that share vocabulary or structure with your input.
- Grammar rules are attached. Each supported language has a curated reference describing its case markers, tense system, negation and word order. The relevant portions are supplied alongside the examples.
- Community corrections are applied. Any correction submitted for a similar input takes priority over the model's own inclination.
- The model generates a translation conditioned on all of the above, rather than on its general knowledge alone.
This is retrieval-augmented translation. It works because the hard part of translating into a low-resource language is not fluency — a large model can produce fluent-sounding text in almost anything — it is knowing the actual forms. Supplying contributed forms at inference time addresses that directly.
Where the data comes from
- Community phrase pairs — sentence pairs contributed by speakers, covering everyday conversation, greetings, commerce, health and travel. Contributions are not individually audited before they go in.
- Published reference grammars — including the Central Institute of Indian Languages adult primer in Sema, and the Sümi Literature Board reader Kichitsahthoh used in Nagaland Board of School Education classes 9 and 10.
- Annotated reading passages — longer texts with speaker-supplied translations and grammatical notes.
- Live corrections — every submission through the Improve button or the contribute form.
The written guides on this site draw on the same material, and each guide lists its sources at the bottom.
Where AI is used, and where it is not
This site should be transparent about this, so:
| Feature | AI involved? |
|---|---|
| Translator output | Yes — generated by a language model conditioned on community data |
| NagaChat replies | Yes — generated by a language model |
| Speech recognition and text-to-speech | Yes — third-party speech models |
| Phrasebook entries | No — human-contributed and reviewed |
| Example sentences in the guides | No — drawn from the community dataset |
| Grammar rules in the guides | No — from published references and speaker corrections |
| Guide prose | Written and edited by a human author, credited on each page |
Machine-generated translations are labelled as such in the interface, and every page carries the reminder that AI translations can be wrong.
Where it fails
An honest account matters more than a confident one, so here are the known weaknesses.
- Long and complex sentences. Retrieval works best when something similar exists in the dataset. Novel, multi-clause sentences fall back on the model's general ability, which for these languages is weak.
- Technical, legal and medical text. The vocabulary is not in the dataset and frequently does not have settled equivalents in the language. Do not use this tool for anything with legal or medical consequences.
- Ao tone. Written Ao does not mark tone, so the training text has lost information the spoken language carries. Output cannot restore it.
- Register and formality. The Nagamese
apni/toidistinction is socially significant and the system does not always get it right. Check it. - Dialect. Ao output follows Chungli; Sümi output follows the standard written form. Speakers of Mongsen, Changki, Lazami or Dayang varieties will find forms that are not theirs.
- Idiom and cultural reference. Word-level accuracy does not produce idiomatic accuracy. A documented case:
kishi-kiabawas rendered as "animistic rituals" where a speaker's correction gave "village fortification". Nothing in a dictionary gets you there.
What happens to the text you submit
Text and audio you submit are sent to third-party AI providers to produce a result, and may be cached briefly to speed up repeated requests. Corrections you deliberately submit are stored and used to improve translation. Ordinary translation input is not published.
Do not paste confidential or personal information into any online translation tool, including this one. Full detail is in the privacy policy.
How to make it better
If you speak any of these languages, the single most useful thing you can do is correct something. The Improve button on any translation, or the contribute form, puts your correction into the dataset that the next person's translation is built from.
Sources and further reading
Grammar descriptions on this page are drawn from the reference material listed below and from the community phrase set behind the translator. Listing a source does not mean every claim here has been checked against it — where they disagree, or where a speaker says otherwise, the speaker is right.
- CIIL adult primer in Sema — Central Institute of Indian Languages
- *Kichitsahthoh*, Sümi reader for classes 9 and 10 — Sümi Literature Board / Nagaland Board of School Education
- NagaTranslate community phrase set and corrections log — community-contributed data