Machine translation (MT) is one of the main tasks in natural language processing whose objective is to translate texts automatically from one natural language to another. Nowadays, using deep neural networks for MT tasks has received great attention. These networks require lots of data to learn abstract representations of the input and store it in continuous vectors. This paper presents the first relatively large-scale Amharic-English parallel sentence dataset. Using these compiled data, we build bi-directional Amharic-English translation models by fine-tuning the existing Facebook M2M100 pre-trained model achieving a BLEU score of 37.79 in Amharic-English 32.74 in English-Amharic translation. Additionally, we explore the effects of Amharic homophone normalization on the machine translation task. The results show that the normalization of Amharic homophone characters increases the performance of Amharic-English machine translation in both directions.

本文介绍了第一个相对较大规模的阿姆哈拉语-英语双语平行语句数据集，利用这些数据，通过微调现有的Facebook M2M100预训练模型，建立了双向阿姆哈拉语-英语翻译模型，在阿姆哈拉语-英语翻译和英语-阿姆哈拉语翻译中均达到了较高的BLEU得分，此外，本文还探讨了阿姆哈拉语同音字范式化对机器翻译的影响。结果表明，阿姆哈拉语同音字的范式化可以提高双向翻译模型的翻译性能。

双向阿姆哈拉语-英语神经机器翻译的标准化效果