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arXiv · 2601.03135

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

Aashish Dhawan·Christopher Driggers-Ellis·Christan Grant·Daisy Zhe Wang
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Abstract

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.

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Aashish Dhawan, Christopher Driggers-Ellis, Christan Grant, Daisy Zhe Wang. 2026-01-10. Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing. https://doi.org/10.18653/v1%2F2026.loresmt-1

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