Abstract
Despite advances in Neural Machine Translation (NMT), low-resource languages like Tigrinya remain underserved due to persistent challenges, including limited corpora, inadequate tokenization strategies, and the lack of standardized evaluation benchmarks. This paper investigates transfer learning techniques using multilingual pretrained models to enhance translation quality for morphologically rich, low-resource languages. We propose a refined approach that integrates language-specific tokenization, informed embedding initialization, and domain-adaptive fine-tuning. To enable rigorous assessment, we construct a high-quality, human-aligned English-Tigrinya evaluation dataset covering diverse domains. Experimental results demonstrate that transfer learning with a custom tokenizer substantially outperforms zero-shot baselines, with gains validated by BLEU, chrF, and qualitative human evaluation. Bonferroni correction is applied to ensure statistical significance across configurations. Error analysis reveals key limitations and informs targeted refinements. This study underscores the importance of linguistically aware modeling and reproducible benchmarks in bridging the performance gap for underrepresented languages. Resources are available at this https URL and this https URL
Abstract (translated)
尽管神经机器翻译（NMT）取得了进展，但由于持续存在的挑战，如有限的语料库、不充分的分词策略和缺乏标准化的评估基准，像提格尼尼亚语这样的低资源语言仍然得不到充分服务。本文探讨了使用多语言预训练模型进行迁移学习技术，以提高形态丰富的低资源语言翻译质量的方法。我们提出了一种经过改进的方法，该方法结合了特定于语言的分词、信息嵌入初始化和领域适应性微调。 为了实现严格的评估，我们构建了一个高质量的人工对齐英提格尼尼亚语评价数据集，涵盖了不同的领域。实验结果表明，在自定义分词器的帮助下进行迁移学习显著优于零样本基准模型，并通过BLEU、chrF和定性的人工评估进行了验证。应用邦弗伦尼校正以确保在不同配置下的统计意义。错误分析揭示了关键的局限性并为针对性改进提供了信息。 这项研究强调了语言意识建模和可重复基准测试在弥合代表性不足的语言性能差距方面的的重要性。相关资源可以在[这里](https://this.url)和[这里](https://that.url)获得。请注意，提供的URL仅为占位符，请访问原文获取实际链接。
URL
https://arxiv.org/abs/2509.20209
