Machine Translation is an effort to bridge language barriers and misinterpretations, making communication more convenient through the automatic translation of languages. The quality of translations produced by corpus-based approaches predominantly depends on the availability of a large parallel corpus. Although machine translation of many Indian languages has progressively gained attention, there is very limited research on machine translation and the challenges of using various machine translation techniques for a low-resource language such as Mizo. In this article, we have implemented and compared statistical-based approaches with modern neural-based approaches for the English–Mizo language pair. We have experimented with different tokenization methods, architectures, and configurations. The performance of translations predicted by the trained models has been evaluated using automatic and human evaluation measures. Furthermore, we have analyzed the prediction errors of the models and the quality of predictions based on variations in sentence length and compared the model performance with the existing baselines.

中文翻译：

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一种改进的英语到米佐神经机器翻译

机器翻译旨在消除语言障碍和误解，通过语言的自动翻译使交流更加方便。基于语料库的方法产生的翻译质量主要取决于大型平行语料库的可用性。尽管许多印度语言的机器翻译逐渐受到关注，但对机器翻译的研究非常有限，并且将各种机器翻译技术用于诸如 Mizo 等资源匮乏的语言的挑战。在本文中，我们针对英语-米佐语言对实施了基于统计的方法并将其与现代基于神经的方法进行了比较。我们已经尝试了不同的标记化方法、架构和配置。已使用自动和人工评估措施对训练模型预测的翻译性能进行了评估。此外，我们分析了模型的预测误差和基于句子长度变化的预测质量，并将模型性能与现有基线进行了比较。