  This paper describes the acquisition, preprocessing, segmentation, and
alignment of an Amharic-English parallel corpus. It will be helpful for machine
translation of a low-resource language, Amharic. We freely released the corpus
for research purposes. Furthermore, we developed baseline statistical and
neural machine translation systems; we trained statistical and neural machine
translation models using the corpus. In the experiments, we also used a large
monolingual corpus for the language model of statistical machine translation
and back-translation of neural machine translation. In the automatic
evaluation, neural machine translation models outperform statistical machine
translation models by approximately six to seven Bilingual Evaluation
Understudy (BLEU) points. Besides, among the neural machine translation models,
the subword models outperform the word-based models by three to four BLEU
points. Moreover, two other relevant automatic evaluation metrics, Translation
Edit Rate on Character Level and Better Evaluation as Ranking, reflect
corresponding differences among the trained models.
