Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911699269844992 |
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| author | Dhawan, Aashish Driggers-Ellis, Christopher Grant, Christan Wang, Daisy Zhe |
| author_facet | Dhawan, Aashish Driggers-Ellis, Christopher Grant, Christan Wang, Daisy Zhe |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_03135 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing Dhawan, Aashish Driggers-Ellis, Christopher Grant, Christan Wang, Daisy Zhe Computation and Language 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. |
| title | Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.03135 |