标题： 使用循环神经网络和迁移学习的低资源神经机器翻译：以英语到伊博语为例 显示英文标题

标题： Low-Resource Neural Machine Translation Using Recurrent Neural Networks and Transfer Learning: A Case Study on English-to-Igbo

摘要： 本研究中，我们开发了用于英语到伊博语（Igbo）翻译的神经机器翻译（NMT）模型和基于Transformer的迁移学习模型——伊博语是一种低资源非洲语言，在尼日利亚及西非地区有超过4000万人使用。 我们的模型是在经过精心整理并作为基准的数据集上进行训练的，该数据集由圣经语料库、本地新闻、维基百科文章和Common Crawl组成，并由母语专家验证。 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框架内应用了基于MarianNMT预训练模型的迁移学习方法。 我们的基于RNN的系统取得了具有竞争力的结果，接近现有的英语-伊博语基准水平。 通过迁移学习，我们观察到BLEU分数提高了+4.83分，达到约70%的翻译准确率。 这些发现强调了将RNN与迁移学习相结合以解决低资源语言翻译任务性能差距的有效性。 显示英文摘要

摘要： 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.