Abstract
In this study, we develop Neural Machine Translation (NMT) and Transformer-based transfer learning models for English-to-Igbo translation - a low-resource African language spoken by over 40 million people across Nigeria and West Africa. Our models are trained on a curated and benchmarked dataset compiled from Bible corpora, local news, Wikipedia articles, and Common Crawl, all verified by native language experts. We leverage Recurrent Neural Network (RNN) architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), enhanced with attention mechanisms to improve translation accuracy. To further enhance performance, we apply transfer learning using MarianNMT pre-trained models within the SimpleTransformers framework. Our RNN-based system achieves competitive results, closely matching existing English-Igbo benchmarks. With transfer learning, we observe a performance gain of +4.83 BLEU points, reaching an estimated translation accuracy of 70%. These findings highlight the effectiveness of combining RNNs with transfer learning to address the performance gap in low-resource language translation tasks.
Abstract (translated)
在这项研究中，我们为英伊博语（Igbo）翻译开发了神经机器翻译（NMT）和基于Transformer的迁移学习模型。伊博语是一种非洲低资源语言，在尼日利亚和西非地区有超过4000万人使用。我们的模型是在精心整理并基准测试过的数据集上进行训练的，该数据集包括从圣经文献、当地新闻、维基百科文章以及Common Crawl收集的数据，并且所有内容都经过了伊博语母语专家的验证。 我们利用循环神经网络（RNN）架构，包括长短期记忆网络（LSTM）和门控循环单元（GRU），并结合注意力机制以提高翻译准确性。为了进一步提升性能，我们在SimpleTransformers框架中应用了使用MarianNMT预训练模型进行迁移学习的方法。基于RNN的系统取得了与现有英伊博语基准相近的竞争性结果。通过引入迁移学习，我们观察到BLEU分数提高了4.83分，估计翻译准确率为70%。 这些发现表明，在低资源语言翻译任务中结合使用RNN和迁移学习能够有效弥补性能差距。
URL
https://arxiv.org/abs/2504.17252
