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.
Abstract (translated)
低资源土著语言通常缺乏有效的神经机器翻译（NMT）所需的平行语料库。合成数据生成为在数据稀缺的情况下缓解这一限制提供了一种实用策略。在这项工作中，我们使用高容量的多语言翻译模型生成合成句子对，以增强美洲土著语言的经过整理的平行数据集。我们将多语言mBART模型针对仅包含经过整理的数据和通过合成增强的数据进行微调，并使用chrF++作为评估指标来衡量翻译质量，这是最近用于黏着语系语言的AmericasNLP共享任务的主要度量标准。我们进一步应用了特定于每种语言的预处理方法，包括正字法规范化和噪声感知过滤，以减少语料库中的伪影。在瓜拉尼语-西班牙语和克丘亚语-西班牙语翻译实验中显示，从合成数据增强中获得了chrF++的一致性改进，而艾马拉语的诊断性试验则突出了通用预处理方法对高度黏着语言的局限性。
URL
https://arxiv.org/abs/2601.03135
