  Many automatic translation works have been addressed between major European
language pairs, by taking advantage of large scale parallel corpora, but very
few research works are conducted on the Amharic-Arabic language pair due to its
parallel data scarcity. Two Long Short-Term Memory (LSTM) and Gated Recurrent
Units (GRU) based Neural Machine Translation (NMT) models are developed using
Attention-based Encoder-Decoder architecture which is adapted from the
open-source OpenNMT system. In order to perform the experiment, a small
parallel Quranic text corpus is constructed by modifying the existing
monolingual Arabic text and its equivalent translation of Amharic language text
corpora available on Tanzile. LSTM and GRU based NMT models and Google
Translation system are compared and found that LSTM based OpenNMT outperforms
GRU based OpenNMT and Google Translation system, with a BLEU score of 12%, 11%,
and 6% respectively.
