  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.

Computation and Language