用合成数据提升美洲原住民语言翻译质量，效果显著。
Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing
- 用多语言模型生成合成语料，扩充原住民语言训练集。
- Guarani-Spanish和Quechua-Spanish翻译任务中chrF++提升明显。
- 针对黏着语特点优化预处理，适合原住民语言研究者。
低资源原住民语言常缺乏神经机器翻译所需的平行语料。本文提出通过高容量多语言翻译模型生成合成句对，来弥补数据稀缺问题。我们对美洲原住民语言的精选平行语料进行合成数据增强，并在仅使用精选数据与合成增强数据上微调多语言mBART模型。采用chrF++作为主要评估指标，该指标被用于近期美洲NLP共享任务中对黏着语的评测。此外，引入语言特定的预处理方法，包括拼写规范化和抗噪声过滤，以减少语料中的异常。在Guarani-Spanish与Quechua-Spanish翻译任务中，合成数据均带来一致的chrF++性能提升；而对Aymara语言的诊断实验显示，通用预处理方法在高度黏着的语言上存在局限性。
原文摘要 · Abstract (English)
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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