  This paper describes the acquisition, preprocessing, segmentation, and
alignment of an Amharic-English parallel corpus. It will be useful for machine
translation of an under-resourced language, Amharic. The corpus is larger than
previously compiled corpora; it is released for research purposes. We trained
neural machine translation and phrase-based statistical machine translation
models using the corpus. In the automatic evaluation, neural machine
translation models outperform phrase-based statistical machine translation
models.
