Abstract
In this paper,we explore the application of Back translation (BT) as a semi-supervised technique to enhance Neural Machine Translation(NMT) models for the English-Luganda language pair, specifically addressing the challenges faced by low-resource languages. The purpose of our study is to demonstrate how BT can mitigate the scarcity of bilingual data by generating synthetic data from monolingual corpora. Our methodology involves developing custom NMT models using both publicly available and web-crawled data, and applying Iterative and Incremental Back translation techniques. We strategically select datasets for incremental back translation across multiple small datasets, which is a novel element of our approach. The results of our study show significant improvements, with translation performance for the English-Luganda pair exceeding previous benchmarks by more than 10 BLEU score units across all translation directions. Additionally, our evaluation incorporates comprehensive assessment metrics such as SacreBLEU, ChrF2, and TER, providing a nuanced understanding of translation quality. The conclusion drawn from our research confirms the efficacy of BT when strategically curated datasets are utilized, establishing new performance benchmarks and demonstrating the potential of BT in enhancing NMT models for low-resource languages.
Abstract (translated)
在这篇论文中，我们探讨了利用反向翻译（Back Translation，BT）作为半监督技术来增强英语-卢甘达语对的神经机器翻译（Neural Machine Translation, NMT）模型的应用，特别关注低资源语言所面临的挑战。我们的研究目的是展示如何通过从单语语料库生成合成数据来缓解双语数据稀缺问题。我们的方法包括使用公开可用和网络爬取的数据开发定制的NMT模型，并应用迭代和增量反向翻译技术。我们在多个小规模数据集中战略性地选择用于增量反向翻译的数据集，这是我们方法的一个新颖元素。 研究结果表明，在所有翻译方向上，英语-卢甘达语对的翻译性能都超过了先前的基准，提高了超过10个BLEU分数单位。此外，我们的评估包括SacreBLEU、ChrF2和TER等全面评估指标，从而为翻译质量提供了细致的理解。根据我们的研究结论，当精心策划的数据集被使用时，BT的有效性得到了确认，这不仅确立了新的性能基准，还展示了BT在增强低资源语言NMT模型方面的潜力。
URL
https://arxiv.org/abs/2505.02463
