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

• URL: http://arxiv.org/abs/2601.03135v2
• Date: Sat, 10 Jan 2026 20:06:32 GMT
• Title: Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing
• Authors: Aashish Dhawan, Christopher Driggers-Ellis, Christan Grant, Daisy Zhe Wang,
• Abstract summary: We augment curated parallel datasets for indigenous languages of the Americas with synthetic sentence pairs generated using a high-capacity multilingual translation model.<n>Experiments on Guarani-Spanish and Quechua-Spanish translation show consistent chrF++ improvements from synthetic data augmentation.<n> diagnostic experiments on Aymara highlight the limitations of generic preprocessing for highly agglutinative languages.
• Score: 5.781452568978427
• License: http://creativecommons.org/licenses/by/4.0/
• 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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