出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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.
