# Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing

arXiv: https://arxiv.org/abs/2601.03135 · HTML: https://mlsanity.com/paper/2601.03135/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers](https://mlsanity.com/paper/2305.19474/) (2023)
- [IndT5: A Text-to-Text Transformer for 10 Indigenous Languages](https://mlsanity.com/paper/2104.07483/) (2021)
- [Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models](https://mlsanity.com/paper/2305.17406/) (2023)
- [Sheffield's Submission to the AmericasNLP Shared Task on Machine Translation into Indigenous Languages](https://mlsanity.com/paper/2306.09830/) (2023)
- [MultiScript30k: Leveraging Multilingual Embeddings to Extend Cross Script Parallel Data](https://mlsanity.com/paper/2512.11074/) (2025)

## Built on by (most cited first, 2 of 2)

- [Retrieval-Augmented Long-Context Translation for Cultural Image Captioning: Gators submission for AmericasNLP 2026 shared task](https://mlsanity.com/paper/2605.20626/) (2026)
- [BM25-Augmented Many-Shot Translation for Low-Resource North-Eastern Indian Languages](https://mlsanity.com/paper/2608.13722/) (2026)

## Abstract (arXiv)

Low-resource indigenous languages often lack the parallel corpora required for effective neural machine translation (NMT). Synthetic data generation offers a practical strategy for mitigating this limitation in data-scarce settings. In this work, we augment curated parallel datasets for indigenous languages of the Americas with synthetic sentence pairs generated using a high-capacity multilingual translation model. We fine-tune a multilingual mBART model on curated-only and synthetically augmented data and evaluate translation quality using chrF++, the primary metric used in recent AmericasNLP shared tasks for agglutinative languages. We further apply language-specific preprocessing, including orthographic normalization and noise-aware filtering, to reduce corpus artifacts. Experiments on Guarani-Spanish and Quechua-Spanish translation show consistent chrF++ improvements from synthetic data augmentation, while diagnostic experiments on Aymara highlight the limitations of generic preprocessing for highly agglutinative languages.