Crowdsourcing Parallel Corpus for English-Oromo Neural Machine
  Translation using Community Engagement Platform
  Even though Afaan Oromo is the most widely spoken language in the Cushitic
family by more than fifty million people in the Horn and East Africa, it is
surprisingly resource-scarce from a technological point of view. The increasing
amount of various useful documents written in English language brings to
investigate the machine that can translate those documents and make it easily
accessible for local language. The paper deals with implementing a translation
of English to Afaan Oromo and vice versa using Neural Machine Translation. But
the implementation is not very well explored due to the limited amount and
diversity of the corpus. However, using a bilingual corpus of just over 40k
sentence pairs we have collected, this study showed a promising result. About a
quarter of this corpus is collected via Community Engagement Platform (CEP)
that was implemented to enrich the parallel corpus through crowdsourcing
translations.
