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

TL;DR

This paper enhances indigenous language machine translation by generating synthetic data, applying language-specific preprocessing, and fine-tuning multilingual models, resulting in improved translation quality for low-resource languages.

Contribution

It introduces a synthetic data augmentation approach combined with language-specific preprocessing to improve NMT for indigenous languages.

Findings

Synthetic data improves translation quality for Guarani and Quechua.

Language-specific preprocessing reduces corpus artifacts.

Limitations exist for highly agglutinative languages like Aymara.

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.