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# LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages

**Authors:** Jared Coleman, Bhaskar Krishnamachari, Ruben Rosales, Khalil Iskarous · **Year:** 2024 · **Venue:** AmericasNLP 2024 (4th Workshop on NLP for Indigenous Languages of the Americas) **Link:** [aclanthology.org/2024.americasnlp-1.9](https://aclanthology.org/2024.americasnlp-1.9/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**8** citations](https://openalex.org/works?filter=cites:W4401042731) openalex · 2026-10-18[**12** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/coleman-2024-llm-assisted-rule-based-machine/) of the 140673 papers whose bibliographies we read[Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)](https://aclanthology.org/volumes/2024.americasnlp-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2024.americasnlp-1.9/)
- [PDF](https://aclanthology.org/2024.americasnlp-1.9.pdf)
- [DOI 10.18653/v1/2024.americasnlp-1.9](https://doi.org/10.18653/v1/2024.americasnlp-1.9)
- [OpenAlex](https://openalex.org/W4401042731)

**Authors (4)**

1. [Jared Coleman](https://openalex.org/A5025634277) [ORCID](https://orcid.org/0000-0003-1227-2962) · h 5 · 71 citations · 34 works
2. [Bhaskar Krishnamachari](https://openalex.org/A5063784062) [ORCID](https://orcid.org/0000-0002-9994-9931) · h 76 · 25005 citations · 591 works
3. [Ruben Rosales](https://openalex.org/A5113211858) h 1 · 8 citations · 5 works
4. [Khalil Iskarous](https://openalex.org/A5044515172) [ORCID](https://orcid.org/0000-0003-3269-7343) · h 19 · 1198 citations · 102 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper builds two teaching translators for Owens Valley Paiute, a critically endangered language with no public corpus. The language-specific part is hand-written: a sentence builder that offers only the choices that keep a sentence valid, given the choices already made, and a deterministic table of suffixes and verbs. A large language model handles the parts the authors could not hand-write: it turns a free English sentence into simple subject-verb or subject-verb-object clauses, and it turns structured records into fluent English. For the Paiute-to-English direction, 98 of 100 random sentences from the builder were translated correctly. For English-to-Paiute, 125 sentences were scored by embedding similarity against the input, the simplified clauses, the comparator (with unknown words removed), and the back-translation. The method needs no parallel data, but it depends on the model for the English side and gives only partial meaning when the vocabulary is missing.

## Ideas

- I1. Make generation constraint-driven. The builder enumerates only the continuations that are licensed by the choices already made. Mova's linearizer in en/src/lin.rs can do the same: a table of valid next slots for each frame, so an invalid order is never produced.
- I2. Use the back-translation as a reference-free check. In normal mode, translate the output back with the same stack and compare it with the source meaning. This is a proxy for the missing reference and it should be reported beside exact match and chrF.
- I3. Calibrate the similarity against a null distribution. The paper sets its threshold at the mean plus three standard deviations of unrelated pairs. For Mova, which is count-based, the null comes from the shuffled-lexicon control: the score of a translation under a shuffled lexicon gives the floor a result must clear.
- I4. Replace unknown words by a typed placeholder in a comparator run. This separates errors caused by a missing lexicon entry from errors of structure. Mova's residual can report unknown-concept tokens as their own class.

## For Mova

The code is tools/translate (the encoder and the linearizer hand-off) and en/src/lin.rs. The language-model parts are not for Mova: at test time the SLM does not call any large model, and a large model is only a teacher. What transfers is the rule-based builder, the constraint-driven generation, and the separation of lexicon gaps from structure errors, which map to the residual classes in measure.rs.

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2024.americasnlp-1.9](https://doi.org/10.18653/v1/2024.americasnlp-1.9)

```
@inproceedings{coleman2024llm,
  title = {{LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages}},
  author = {Coleman, Jared and Krishnamachari, Bhaskar and Rosales, Ruben and Iskarous, Khalil},
  booktitle = {Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)},
  year = {2024},
  doi = {10.18653/v1/2024.americasnlp-1.9},
  url = {https://doi.org/10.18653/v1/2024.americasnlp-1.9}
}
```

- [← previous Czech-English Dependency Tree-based Machine Translation](https://md.mova.elamur.ai/free/en/docs/papers/cmejrek-2003-czech-english-dependency-tree-based/)
- [next → Translation using Minimal Recursion Semantics](https://md.mova.elamur.ai/free/en/docs/papers/copestake-1995-translation-minimal-recursion-semantics/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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